Weight edits to reduce repetition in math reasoning

Summary

In Progress

Understand
Analyzed model failures and activation patterns.
Understand
Build
Applied targeted rank-one weight edits to reduce repetition.
Build
Verify
Validated edits on held-out math problem sets.
Verify
Understand
Diagnosed infrastructure failures during validation.
Understand
Verify
Retried validation after infrastructure issues.
Verify

TASK. You are given the weights of a language model, Qwen3.5-0.8B (text part: 24 layers of width 1,024; layers 3, 7, 11, 15, 19 and 23 are full-attention layers with 8 query heads and 2 key/value heads of size 256 and a gated output, the other 18 are Gated DeltaNet linear-attention layers with 16 heads of size 128; each layer has a SwiGLU MLP of width 3,584; RMSNorm; vocabulary 248,320; input embedding and output layer tied). Your job is to make it better at mathematics by EDITING ITS WEIGHTS DIRECTLY: find out what goes wrong inside the network when it fails at math problems and repair that with targeted changes.

HOW IT WILL BE MEASURED. After you finish, your edited model and the unedited model are evaluated on MATH-500 and GSM8K with this fixed protocol: the chat template with thinking enabled (enable_thinking=True: the model reasons between and , then answers); the user message is the problem, a newline and "Please reason step by step, and put your final answer within \boxed{}."; sampling with temperature 0.6, top_p 0.95, top_k 20, presence penalty 1.5 and at most 32,768 new tokens for reasoning and answer together; the last \boxed{} answer is graded against the reference answer. The config, tokenizer, chat template and decoding settings are fixed: only weight values can change (same tensor names and shapes). You never see these benchmarks.

WHAT COUNTS. We want edits that come from understanding the network: find out what goes wrong when it fails at math problems, identify the components responsible (specific neurons, heads, directions or circuits) and change them in a way you can justify with evidence. Repairing a specific failure mechanism is in scope: for example, if the model often fails because it repeats itself and never finishes, finding and fixing what drives that repetition counts. Out of scope, and refused by save_edit: the final norm (model.language_model.norm.weight), the input embedding / output layer (model.language_model.embed_tokens.weight, lm_head.weight), and any edit that only multiplies a whole tensor by a constant. Global knobs (making the whole output distribution sharper or flatter) are not what we are looking for, and neither is making the model skip its thinking or close it early regardless of the problem; repairing a specific failure inside the thinking (such as repetition) is in scope.

WHAT YOU HAVE.

A persistent Python kernel (no internet, no GPU): variables survive between your code blocks, like a notebook. The model's files are read-only in /model, practice problems read-only in /practice, and /work is your read-write folder. Available: numpy, scipy, scikit-learn, PyTorch (CPU), pandas, sympy, networkx, safetensors, tokenizers. Defined in advance: NAMES (all tensor names), load(name) (a float32 torch tensor: your current edit if there is one, else the original), load_original(name), save_edit(name, tensor), drop_edit(name), edited() and tok (the tokenizer). Gradients are disabled. To run code, reply with exactly one block that starts with ```python and nothing else; if its last line is an expression, its value is shown. One execution may take up to 900 seconds and 96 GB; output is cut at 10,000 characters. You may run code up to 300 times. If the kernel is restarted (time or memory limit), its variables are lost and you are told so.
Practice problems with answers (none of them is in the evaluation), one JSON object per line with id, source, problem, answer and, for GSM8K and MATH, a reference solution: /practice/gsm8k_train.jsonl (7,469 grade-school problems), /practice/math_train.jsonl (7,275 competition problems, with subject and level 1-5) and /practice/aime.jsonl (714 AIME problems, 1983-2023, answers only). Choose whichever serve your experiments.
A forward-pass tool with a budget of 10 forward passes in total. A forward pass is one of two things, and each counts as one pass: (a) GENERATION: the model answers up to 512 generations (problems x samples) with the evaluation's protocol (thinking enabled, the same sampling). Reply with exactly one block that starts with forward and contains JSON: {"type": "generate", "model": "original", "edited" or "both", "problems": ["<practice id>", ...], "prompts": ["<your own user message>", ...] (optional), "n": samples per item (items x n <= 512; with "both" <= 256 per model), "max_tokens": at most 32768, "activations": {"layers": [layer indices 0..24], "mlp": true or false} (optional)} "original" runs the unedited weights, "edited" your current edits, and "both" runs both on the same items and returns a paired comparison (the accuracy difference edited - original over items with its standard error, and how many samples went from wrong to right and back). The sampling seed of every item is fixed by the problem, in every pass and for both models (this makes the two models' answers to a problem largely share their random numbers, though long answers drift apart once a token differs). Every answer to a practice problem is graded. You get the accuracy (overall and by source), the mean length, the share cut at max_tokens and the failure counts; texts, extracted answers and grades are in /work/gen_<k>.jsonl (with "both": /work/gen_<k>_original.jsonl and /work/gen_<k>_edited.jsonl). With "activations", for every generated answer the model's residual stream at the requested layers (0 = embeddings, l = after layer l) is saved, taken at the last token of the prompt and averaged over the answer's first 8,192 tokens (and the MLPs' down_proj inputs with "mlp": true), with each answer's grade: /work/gen_<k>_acts/ (index.json lists the files and samples; with "both" one folder per model). (b) TEACHER-FORCED: the model reads up to 1,024 texts of up to 32,768 tokens each, at most 524,288 tokens in total (no generation; it takes seconds to a few minutes). Reply with a forward block containing JSON: {"type": "teacher_forced", "model": "original" or "edited", "texts": ["raw text", ...] or "chats": [[{"role": "user", "content": "..."}, {"role": "assistant", "content": "..."}], ...] (add "enable_thinking": true for the thinking template), "return": {"token_logprobs": true, "topk": k (at most 50), "hidden_states": [layers 0..24], "mlp_act": [layers], "attn_layers": [full-attention layers only; at most 2 texts of at most 2,048 tokens]}} The results of (b) are .npy files (with meta.json) in /work/fwd_/, right-padded to the longest text (at most 6 GB per pass). A request that fails does not count against the budget. A generation pass takes up to about half an hour at full size (answers can be 32,768 tokens long).

HOW TO WORK.

Diagnose first. Look at how the model fails on practice problems: a wrong final answer, finishing without an answer, or being cut off at the token limit (often while repeating itself in its thinking). The generation summary counts these; read some failing texts. Go after the largest category you can explain.
Localize causally. That a component is more active on correct answers is not enough (correct answers come from easier and shorter problems). Before relying on a component, show that changing it changes the behaviour you predicted, and try several strengths to see a dose-response.
Measure with enough power. Accuracy over n generations has a standard error of about 50/sqrt(n) percentage points: about 4.4 points at 128 generations, 2.2 at 512. Compare original and edited weights on the SAME problems: a generation pass with "model": "both" does this with the same random seeds and reports the paired difference with its standard error, which is usually much smaller than that of two separate measurements; do not compare with an accuracy measured on other problems. Keep an edit only if its gain is clearly larger than its standard error on problems that were not used to choose it. A lower share of cut-off answers does not by itself mean more correct answers: judge edits by accuracy. Dense measurements help too and are cheap: one teacher-forced pass can read the reference solutions of up to 1,024 practice problems (or the model's own good versus failing continuations) and gives a signal on every token. Edits that change a tensor by a fraction of a percent usually have effects too small to see; test strengths large enough to matter and back off if they hurt.
Plan your forward passes (see the budget above): for example one diagnostic generation with activations, teacher-forced passes for dense paired measurements, and a final paired generation ("model": "both") on problems you have not used to confirm.
Work efficiently: state the hypothesis of each code block in its first comment line; work through the model in parts; load things once into kernel variables; print compact summaries, never whole arrays (and never raw lists of vocabulary tokens: the vocabulary contains offensive and adult strings and printing them can get the session refused by the provider's content filter; print digits, math symbols, common math words or token ids instead; outputs are shown in ASCII only, other characters appear as '?'; likewise print short excerpts of generated texts, at most about 300 characters each, not whole transcripts); keep /work/notes.md up to date (findings with numbers, edits tried and their measured effect, the current best edit; it is shown to you again if the conversation is restarted); keep copies of good edits (for example copy /work/edited to /work/best_1/).

RULES. Not allowed: (a) gradient descent or any other training: no backward passes, autograd or optimizers, and no iterative fitting of weights to a loss; (b) running the model other than through the forward-pass tool: do not implement or simulate the network's computation on any input in the kernel; (c) internet access or search (there is none). Allowed: any analysis of the weights and of forward-pass results, any use of the practice problems, and any direct edit of the weights that you compute and that is in scope.

HOW THE SESSION RUNS. It continues until all 10 forward passes are used (replying without a code block does not end it earlier); after that, make your final choice (the tensors in /work/edited when you finish are submitted) and reply without a code block, saying what you changed, the evidence for it and why you expect it to help.

python

# Hypothesis: baseline math failures can be localized by combining failure types with layerwise and MLP activation differences.import os, json, math, time, collectionsimport numpy as npimport torchfrom pathlib import Pathprint("Model files:", os.listdir("/model"))print("Practice files:", os.listdir("/practice"))print("Tensor count:", len(NAMES))print("Representative tensors:")for name in NAMES:    if ".layers.0." in name or ".layers.3." in name:        print(name, tuple(load_original(name).shape))practice = {}for file in ["gsm8k_train.jsonl", "math_train.jsonl", "aime.jsonl"]:    with open("/practice/" + file) as f:        rows = [json.loads(line) for line in f]    practice[file] = rows    print(file, len(rows), "fields:", list(rows[0]), "first ids:", [r["id"] for r in rows[:3]])print("Existing edits:", edited())

# Hypothesis: baseline math failures can be localized by combining failure types with layerwise and MLP activation differences.import os, json, math, time, collectionsimport numpy as npimport torchfrom pathlib import Pathprint("Model files:", os.listdir("/model"))print("Practice files:", os.listdir("/practice"))print("Tensor count:", len(NAMES))print("Representative tensors:")for name in NAMES:    if ".layers.0." in name or ".layers.3." in name:        print(name, tuple(load_original(name).shape))practice = {}for file in ["gsm8k_train.jsonl", "math_train.jsonl", "aime.jsonl"]:    with open("/practice/" + file) as f:        rows = [json.loads(line) for line in f]    practice[file] = rows    print(file, len(rows), "fields:", list(rows[0]), "first ids:", [r["id"] for r in rows[:3]])print("Existing edits:", edited())

OUTPUT of Python run 1 of at most 300:

Model files: ['generation_config.json', 'model.safetensors', 'tokenizer_config.json', 'tokenizer.json', 'chat_template.jinja', 'config.json']Practice files: ['gsm8k_train.jsonl', 'math_train.jsonl', 'stats.json', 'README.txt', 'aime.jsonl']Tensor count: 320Representative tensors:model.language_model.layers.0.input_layernorm.weight (1024,)model.language_model.layers.0.linear_attn.A_log (16,)model.language_model.layers.0.linear_attn.conv1d.weight (6144, 1, 4)model.language_model.layers.0.linear_attn.dt_bias (16,)model.language_model.layers.0.linear_attn.in_proj_a.weight (16, 1024)model.language_model.layers.0.linear_attn.in_proj_b.weight (16, 1024)model.language_model.layers.0.linear_attn.in_proj_qkv.weight (6144, 1024)model.language_model.layers.0.linear_attn.in_proj_z.weight (2048, 1024)model.language_model.layers.0.linear_attn.norm.weight (128,)model.language_model.layers.0.linear_attn.out_proj.weight (1024, 2048)model.language_model.layers.0.mlp.down_proj.weight (1024, 3584)model.language_model.layers.0.mlp.gate_proj.weight (3584, 1024)model.language_model.layers.0.mlp.up_proj.weight (3584, 1024)model.language_model.layers.0.post_attention_layernorm.weight (1024,)model.language_model.layers.3.input_layernorm.weight (1024,)model.language_model.layers.3.mlp.down_proj.weight (1024, 3584)model.language_model.layers.3.mlp.gate_proj.weight (3584, 1024)model.language_model.layers.3.mlp.up_proj.weight (3584, 1024)model.language_model.layers.3.post_attention_layernorm.weight (1024,)model.language_model.layers.3.self_attn.k_norm.weight (256,)model.language_model.layers.3.self_attn.k_proj.weight (512, 1024)model.language_model.layers.3.self_attn.o_proj.weight (1024, 2048)model.language_model.layers.3.self_attn.q_norm.weight (256,)model.language_model.layers.3.self_attn.q_proj.weight (4096, 1024)model.language_model.layers.3.self_attn.v_proj.weight (512, 1024)gsm8k_train.jsonl 7469 fields: ['id', 'source', 'problem', 'answer', 'solution'] first ids: ['gsm8k_train_0', 'gsm8k_train_1', 'gsm8k_train_2']math_train.jsonl 7275 fields: ['id', 'source', 'subject', 'level', 'problem', 'answer', 'solution'] first ids: ['math_train_algebra_0', 'math_train_algebra_1', 'math_train_algebra_2']aime.jsonl 714 fields: ['id', 'source', 'problem', 'answer', 'year'] first ids: ['aime_1983-1', 'aime_1983-3', 'aime_1983-4']Existing edits: []

Model files: ['generation_config.json', 'model.safetensors', 'tokenizer_config.json', 'tokenizer.json', 'chat_template.jinja', 'config.json']Practice files: ['gsm8k_train.jsonl', 'math_train.jsonl', 'stats.json', 'README.txt', 'aime.jsonl']Tensor count: 320Representative tensors:model.language_model.layers.0.input_layernorm.weight (1024,)model.language_model.layers.0.linear_attn.A_log (16,)model.language_model.layers.0.linear_attn.conv1d.weight (6144, 1, 4)model.language_model.layers.0.linear_attn.dt_bias (16,)model.language_model.layers.0.linear_attn.in_proj_a.weight (16, 1024)model.language_model.layers.0.linear_attn.in_proj_b.weight (16, 1024)model.language_model.layers.0.linear_attn.in_proj_qkv.weight (6144, 1024)model.language_model.layers.0.linear_attn.in_proj_z.weight (2048, 1024)model.language_model.layers.0.linear_attn.norm.weight (128,)model.language_model.layers.0.linear_attn.out_proj.weight (1024, 2048)model.language_model.layers.0.mlp.down_proj.weight (1024, 3584)model.language_model.layers.0.mlp.gate_proj.weight (3584, 1024)model.language_model.layers.0.mlp.up_proj.weight (3584, 1024)model.language_model.layers.0.post_attention_layernorm.weight (1024,)model.language_model.layers.3.input_layernorm.weight (1024,)model.language_model.layers.3.mlp.down_proj.weight (1024, 3584)model.language_model.layers.3.mlp.gate_proj.weight (3584, 1024)model.language_model.layers.3.mlp.up_proj.weight (3584, 1024)model.language_model.layers.3.post_attention_layernorm.weight (1024,)model.language_model.layers.3.self_attn.k_norm.weight (256,)model.language_model.layers.3.self_attn.k_proj.weight (512, 1024)model.language_model.layers.3.self_attn.o_proj.weight (1024, 2048)model.language_model.layers.3.self_attn.q_norm.weight (256,)model.language_model.layers.3.self_attn.q_proj.weight (4096, 1024)model.language_model.layers.3.self_attn.v_proj.weight (512, 1024)gsm8k_train.jsonl 7469 fields: ['id', 'source', 'problem', 'answer', 'solution'] first ids: ['gsm8k_train_0', 'gsm8k_train_1', 'gsm8k_train_2']math_train.jsonl 7275 fields: ['id', 'source', 'subject', 'level', 'problem', 'answer', 'solution'] first ids: ['math_train_algebra_0', 'math_train_algebra_1', 'math_train_algebra_2']aime.jsonl 714 fields: ['id', 'source', 'problem', 'answer', 'year'] first ids: ['aime_1983-1', 'aime_1983-3', 'aime_1983-4']Existing edits: []

python

# Hypothesis: a source-balanced diagnostic sample will distinguish arithmetic/reasoning errors from pathological repetition without selection by outcome.rng = np.random.default_rng(81273)gsm_diag = rng.choice(len(practice["gsm8k_train.jsonl"]), 96, replace=False)math_diag = rng.choice(len(practice["math_train.jsonl"]), 160, replace=False)diag_ids = [practice["gsm8k_train.jsonl"][i]["id"] for i in gsm_diag]diag_ids += [practice["math_train.jsonl"][i]["id"] for i in math_diag]by_id = {r["id"]: r for rows in practice.values() for r in rows}print(json.dumps(diag_ids))print("MATH diagnostic levels:", collections.Counter(str(by_id[i]["level"]) for i in diag_ids if by_id[i]["source"] != "gsm8k"))

# Hypothesis: a source-balanced diagnostic sample will distinguish arithmetic/reasoning errors from pathological repetition without selection by outcome.rng = np.random.default_rng(81273)gsm_diag = rng.choice(len(practice["gsm8k_train.jsonl"]), 96, replace=False)math_diag = rng.choice(len(practice["math_train.jsonl"]), 160, replace=False)diag_ids = [practice["gsm8k_train.jsonl"][i]["id"] for i in gsm_diag]diag_ids += [practice["math_train.jsonl"][i]["id"] for i in math_diag]by_id = {r["id"]: r for rows in practice.values() for r in rows}print(json.dumps(diag_ids))print("MATH diagnostic levels:", collections.Counter(str(by_id[i]["level"]) for i in diag_ids if by_id[i]["source"] != "gsm8k"))

OUTPUT of Python run 2 of at most 300:

["gsm8k_train_7354", "gsm8k_train_4960", "gsm8k_train_4335", "gsm8k_train_3553", "gsm8k_train_5789", "gsm8k_train_1381", "gsm8k_train_1927", "gsm8k_train_5240", "gsm8k_train_2157", "gsm8k_train_494", "gsm8k_train_7076", "gsm8k_train_6526", "gsm8k_train_422", "gsm8k_train_975", "gsm8k_train_1387", "gsm8k_train_5866", "gsm8k_train_462", "gsm8k_train_2739", "gsm8k_train_0", "gsm8k_train_5989", "gsm8k_train_1028", "gsm8k_train_714", "gsm8k_train_7181", "gsm8k_train_350", "gsm8k_train_7198", "gsm8k_train_3835", "gsm8k_train_2079", "gsm8k_train_5000", "gsm8k_train_7182", "gsm8k_train_2193", "gsm8k_train_576", "gsm8k_train_1098", "gsm8k_train_7257", "gsm8k_train_4015", "gsm8k_train_4296", "gsm8k_train_4890", "gsm8k_train_8", "gsm8k_train_900", "gsm8k_train_5039", "gsm8k_train_3284", "gsm8k_train_2723", "gsm8k_train_6392", "gsm8k_train_4875", "gsm8k_train_4126", "gsm8k_train_5313", "gsm8k_train_4620", "gsm8k_train_5319", "gsm8k_train_3834", "gsm8k_train_817", "gsm8k_train_5767", "gsm8k_train_6980", "gsm8k_train_7375", "gsm8k_train_1451", "gsm8k_train_5547", "gsm8k_train_1201", "gsm8k_train_2860", "gsm8k_train_6247", "gsm8k_train_1766", "gsm8k_train_4352", "gsm8k_train_5327", "gsm8k_train_6533", "gsm8k_train_4082", "gsm8k_train_3281", "gsm8k_train_1547", "gsm8k_train_4044", "gsm8k_train_1707", "gsm8k_train_297", "gsm8k_train_1853", "gsm8k_train_76", "gsm8k_train_1737", "gsm8k_train_3561", "gsm8k_train_7435", "gsm8k_train_4799", "gsm8k_train_2159", "gsm8k_train_6726", "gsm8k_train_700", "gsm8k_train_3113", "gsm8k_train_506", "gsm8k_train_3886", "gsm8k_train_5132", "gsm8k_train_850", "gsm8k_train_1016", "gsm8k_train_3378", "gsm8k_train_1993", "gsm8k_train_5910", "gsm8k_train_48", "gsm8k_train_2353", "gsm8k_train_1101", "gsm8k_train_327", "gsm8k_train_4653", "gsm8k_train_6380", "gsm8k_train_54", "gsm8k_train_2985", "gsm8k_train_1700", "gsm8k_train_4276", "gsm8k_train_5840", "math_train_precalculus_587", "math_train_geometry_350", "math_train_intermediate_algebra_1136", "math_train_algebra_603", "math_train_prealgebra_68", "math_train_intermediate_algebra_895", "math_train_intermediate_algebra_497", "math_train_counting_and_probability_685", "math_train_algebra_635", "math_train_geometry_67", "math_train_prealgebra_321", "math_train_prealgebra_831", "math_train_algebra_469", "math_train_counting_and_probability_483", "math_train_algebra_241", "math_train_counting_and_probability_527", "math_train_counting_and_probability_644", "math_train_algebra_209", "math_train_intermediate_algebra_732", "math_train_number_theory_174", "math_train_algebra_1097", "math_train_intermediate_algebra_458", "math_train_algebra_317", "math_train_prealgebra_551", "math_train_prealgebra_1170", "math_train_algebra_1173", "math_train_number_theory_425", "math_train_counting_and_probability_666", "math_train_geometry_200", "math_train_precalculus_176", "math_train_intermediate_algebra_776", "math_train_counting_and_probability_278", "math_train_geometry_226", "math_train_algebra_1454", "math_train_geometry_837", "math_train_geometry_694", "math_train_algebra_349", "math_train_counting_and_probability_181", "math_train_algebra_388", "math_train_intermediate_algebra_225", "math_train_geometry_481", "math_train_algebra_608", "math_train_algebra_1017", "math_train_geometry_249", "math_train_geometry_645", "math_train_intermediate_algebra_1190", "math_train_prealgebra_38", "math_train_algebra_244", "math_train_geometry_642", "math_train_precalculus_662", "math_train_intermediate_algebra_1243", "math_train_prealgebra_1038", "math_train_prealgebra_1165", "math_train_algebra_1195", "math_train_number_theory_762", "math_train_prealgebra_1153", "math_train_algebra_1112", "math_train_geometry_618", "math_train_prealgebra_618", "math_train_number_theory_330", "math_train_intermediate_algebra_700", "math_train_prealgebra_486", "math_train_intermediate_algebra_218", "math_train_counting_and_probability_705", "math_train_algebra_641", "math_train_intermediate_algebra_1137", "math_train_geometry_175", "math_train_geometry_409", "math_train_precalculus_565", "math_train_algebra_720", "math_train_intermediate_algebra_865", "math_train_algebra_791", "math_train_counting_and_probability_433", "math_train_prealgebra_118", "math_train_algebra_1265", "math_train_counting_and_probability_53", "math_train_geometry_223", "math_train_algebra_1108", "math_train_precalculus_77", "math_train_algebra_412", "math_train_precalculus_659", "math_train_algebra_6", "math_train_prealgebra_1029", "math_train_prealgebra_972", "math_train_geometry_18", "math_train_number_theory_508", "math_train_number_theory_806", "math_train_intermediate_algebra_1050", "math_train_algebra_993", "math_train_intermediate_algebra_742", "math_train_geometry_82", "math_train_algebra_1691", "math_train_intermediate_algebra_693", "math_train_geometry_679", "math_train_prealgebra_32", "math_train_counting_and_probability_478", "math_train_algebra_1619", "math_train_algebra_340", "math_train_precalculus_288", "math_train_geometry_379", "math_train_precalculus_41", "math_train_intermediate_algebra_120", "math_train_counting_and_probability_119", "math_train_number_theory_390", "math_train_algebra_426", "math_train_algebra_187", "math_train_geometry_440", "math_train_geometry_289", "math_train_counting_and_probability_3", "math_train_number_theory_244", "math_train_geometry_724", "math_train_algebra_329", "math_train_counting_and_probability_254", "math_train_algebra_1144", "math_train_algebra_170", "math_train_prealgebra_479", "math_train_intermediate_algebra_250", "math_train_prealgebra_1035", "math_train_number_theory_205", "math_train_algebra_801", "math_train_algebra_962", "math_train_prealgebra_111", "math_train_prealgebra_584", "math_train_counting_and_probability_683", "math_train_counting_and_probability_648", "math_train_algebra_21", "math_train_algebra_292", "math_train_algebra_1094", "math_train_algebra_261", "math_train_precalculus_694", "math_train_algebra_824", "math_train_intermediate_algebra_829", "math_train_number_theory_84", "math_train_number_theory_620", "math_train_algebra_8", "math_train_algebra_398", "math_train_prealgebra_444", "math_train_intermediate_algebra_485", "math_train_counting_and_probability_385", "math_train_algebra_975", "math_train_algebra_1243", "math_train_number_theory_616", "math_train_algebra_1074", "math_train_counting_and_probability_570", "math_train_algebra_1105", "math_train_algebra_1551", "math_train_counting_and_probability_304", "math_train_algebra_1064", "math_train_prealgebra_1025", "math_train_prealgebra_955", "math_train_intermediate_algebra_490", "math_train_prealgebra_672", "math_train_number_theory_775", "math_train_intermediate_algebra_929", "math_train_prealgebra_502", "math_train_geometry_614", "math_train_intermediate_algebra_638", "math_train_precalculus_531", "math_train_geometry_693", "math_train_algebra_1190"]MATH diagnostic levels: Counter({'Level 5': 52, 'Level 4': 38, 'Level 3': 34, 'Level 2': 22, 'Level 1': 14})

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"math_train_algebra_1190"]MATH diagnostic levels: Counter({'Level 5': 52, 'Level 4': 38, 'Level 3': 34, 'Level 2': 22, 'Level 1': 14})

forward

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{"type":"generate","model":"original","problems":["gsm8k_train_7354","gsm8k_train_4960","gsm8k_train_4335","gsm8k_train_3553","gsm8k_train_5789","gsm8k_train_1381","gsm8k_train_1927","gsm8k_train_5240","gsm8k_train_2157","gsm8k_train_494","gsm8k_train_7076","gsm8k_train_6526","gsm8k_train_422","gsm8k_train_975","gsm8k_train_1387","gsm8k_train_5866","gsm8k_train_462","gsm8k_train_2739","gsm8k_train_0","gsm8k_train_5989","gsm8k_train_1028","gsm8k_train_714","gsm8k_train_7181","gsm8k_train_350","gsm8k_train_7198","gsm8k_train_3835","gsm8k_train_2079","gsm8k_train_5000","gsm8k_train_7182","gsm8k_train_2193","gsm8k_train_576","gsm8k_train_1098","gsm8k_train_7257","gsm8k_train_4015","gsm8k_train_4296","gsm8k_train_4890","gsm8k_train_8","gsm8k_train_900","gsm8k_train_5039","gsm8k_train_3284","gsm8k_train_2723","gsm8k_train_6392","gsm8k_train_4875","gsm8k_train_4126","gsm8k_train_5313","gsm8k_train_4620","gsm8k_train_5319","gsm8k_train_3834","gsm8k_train_817","gsm8k_train_5767","gsm8k_train_6980","gsm8k_train_7375","gsm8k_train_1451","gsm8k_train_5547","gsm8k_train_1201","gsm8k_train_2860","gsm8k_train_6247","gsm8k_train_1766","gsm8k_train_4352","gsm8k_train_5327","gsm8k_train_6533","gsm8k_train_4082","gsm8k_train_3281","gsm8k_train_1547","gsm8k_train_4044","gsm8k_train_1707","gsm8k_train_297","gsm8k_train_1853","gsm8k_train_76","gsm8k_train_1737","gsm8k_train_3561","gsm8k_train_7435","gsm8k_train_4799","gsm8k_train_2159","gsm8k_train_6726","gsm8k_train_700","gsm8k_train_3113","gsm8k_train_506","gsm8k_train_3886","gsm8k_train_5132","gsm8k_train_850","gsm8k_train_1016","gsm8k_train_3378","gsm8k_train_1993","gsm8k_train_5910","gsm8k_train_48","gsm8k_train_2353","gsm8k_train_1101","gsm8k_train_327","gsm8k_train_4653","gsm8k_train_6380","gsm8k_train_54","gsm8k_train_2985","gsm8k_train_1700","gsm8k_train_4276","gsm8k_train_5840","math_train_precalculus_587","math_train_geometry_350","math_train_intermediate_algebra_1136","math_train_algebra_603","math_train_prealgebra_68","math_train_intermediate_algebra_895","math_train_intermediate_algebra_497","math_train_counting_and_probability_685","math_train_algebra_635","math_train_geometry_67","math_train_prealgebra_321","math_train_prealgebra_831","math_train_algebra_469","math_train_counting_and_probability_483","math_train_algebra_241","math_train_counting_and_probability_527","math_train_counting_and_probability_644","math_train_algebra_209","math_train_intermediate_algebra_732","math_train_number_theory_174","math_train_algebra_1097","math_train_intermediate_algebra_458","math_train_algebra_317","math_train_prealgebra_551","math_train_prealgebra_1170","math_train_algebra_1173","math_train_number_theory_425","math_train_counting_and_probability_666","math_train_geometry_200","math_train_precalculus_176","math_train_intermediate_algebra_776","math_train_counting_and_probability_278","math_train_geometry_226","math_train_algebra_1454","math_train_geometry_837","math_train_geometry_694","math_train_algebra_349","math_train_counting_and_probability_181","math_train_algebra_388","math_train_intermediate_algebra_225","math_train_geometry_481","math_train_algebra_608","math_train_algebra_1017","math_train_geometry_249","math_train_geometry_645","math_train_intermediate_algebra_1190","math_train_prealgebra_38","math_train_algebra_244","math_train_geometry_642","math_train_precalculus_662","math_train_intermediate_algebra_1243","math_train_prealgebra_1038","math_train_prealgebra_1165","math_train_algebra_1195","math_train_number_theory_762","math_train_prealgebra_1153","math_train_algebra_1112","math_train_geometry_618","math_train_prealgebra_618","math_train_number_theory_330","math_train_intermediate_algebra_700","math_train_prealgebra_486","math_train_intermediate_algebra_218","math_train_counting_and_probability_705","math_train_algebra_641","math_train_intermediate_algebra_1137","math_train_geometry_175","math_train_geometry_409","math_train_precalculus_565","math_train_algebra_720","math_train_intermediate_algebra_865","math_train_algebra_791","math_train_counting_and_probability_433","math_train_prealgebra_118","math_train_algebra_1265","math_train_counting_and_probability_53","math_train_geometry_223","math_train_algebra_1108","math_train_precalculus_77","math_train_algebra_412","math_train_precalculus_659","math_train_algebra_6","math_train_prealgebra_1029","math_train_prealgebra_972","math_train_geometry_18","math_train_number_theory_508","math_train_number_theory_806","math_train_intermediate_algebra_1050","math_train_algebra_993","math_train_intermediate_algebra_742","math_train_geometry_82","math_train_algebra_1691","math_train_intermediate_algebra_693","math_train_geometry_679","math_train_prealgebra_32","math_train_counting_and_probability_478","math_train_algebra_1619","math_train_algebra_340","math_train_precalculus_288","math_train_geometry_379","math_train_precalculus_41","math_train_intermediate_algebra_120","math_train_counting_and_probability_119","math_train_number_theory_390","math_train_algebra_426","math_train_algebra_187","math_train_geometry_440","math_train_geometry_289","math_train_counting_and_probability_3","math_train_number_theory_244","math_train_geometry_724","math_train_algebra_329","math_train_counting_and_probability_254","math_train_algebra_1144","math_train_algebra_170","math_train_prealgebra_479","math_train_intermediate_algebra_250","math_train_prealgebra_1035","math_train_number_theory_205","math_train_algebra_801","math_train_algebra_962","math_train_prealgebra_111","math_train_prealgebra_584","math_train_counting_and_probability_683","math_train_counting_and_probability_648","math_train_algebra_21","math_train_algebra_292","math_train_algebra_1094","math_train_algebra_261","math_train_precalculus_694","math_train_algebra_824","math_train_intermediate_algebra_829","math_train_number_theory_84","math_train_number_theory_620","math_train_algebra_8","math_train_algebra_398","math_train_prealgebra_444","math_train_intermediate_algebra_485","math_train_counting_and_probability_385","math_train_algebra_975","math_train_algebra_1243","math_train_number_theory_616","math_train_algebra_1074","math_train_counting_and_probability_570","math_train_algebra_1105","math_train_algebra_1551","math_train_counting_and_probability_304","math_train_algebra_1064","math_train_prealgebra_1025","math_train_prealgebra_955","math_train_intermediate_algebra_490","math_train_prealgebra_672","math_train_number_theory_775","math_train_intermediate_algebra_929","math_train_prealgebra_502","math_train_geometry_614","math_train_intermediate_algebra_638","math_train_precalculus_531","math_train_geometry_693","math_train_algebra_1190"],"n":1,"max_tokens":32768,"activations":{"layers":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24],"mlp":true}}

Forward pass 1 of 10 done (generate); results in /work/gen_1.jsonl. Summary:

{ "activations": {  "dir": "/work/gen_1_acts/",  "files": {   "hidden_last_prompt.npy": [    256,    24,    1024   ],   "hidden_mean_answer.npy": [    256,    24,    1024   ],   "mlp_last_prompt.npy": [    256,    23,    3584   ],   "mlp_mean_answer.npy": [    256,    23,    3584   ]  } }, "ok": true, "type": "generate", "model": "original", "file": "/work/gen_1.jsonl", "generations": 256, "graded": 256, "accuracy": 0.09375, "accuracy_by_source": {  "gsm8k": 0.125,  "math": 0.075 }, "mean_tokens": 30428, "cut_share": 0.9141, "failures": {  "wrong_answer": 0,  "no_answer_finished": 0,  "cut_at_max_tokens": 232 }, "s": 719.1}

{ "activations": {  "dir": "/work/gen_1_acts/",  "files": {   "hidden_last_prompt.npy": [    256,    24,    1024   ],   "hidden_mean_answer.npy": [    256,    24,    1024   ],   "mlp_last_prompt.npy": [    256,    23,    3584   ],   "mlp_mean_answer.npy": [    256,    23,    3584   ]  } }, "ok": true, "type": "generate", "model": "original", "file": "/work/gen_1.jsonl", "generations": 256, "graded": 256, "accuracy": 0.09375, "accuracy_by_source": {  "gsm8k": 0.125,  "math": 0.075 }, "mean_tokens": 30428, "cut_share": 0.9141, "failures": {  "wrong_answer": 0,  "no_answer_finished": 0,  "cut_at_max_tokens": 232 }, "s": 719.1}

Forward passes left: 9.

python

# Hypothesis: the dominant failure is a recurrent thinking loop, not missing mathematical knowledge (232/256 answers hit the token limit).gen1 = [json.loads(s) for s in open("/work/gen_1.jsonl")]print("Generation fields:", list(gen1[0]))idx1 = json.load(open("/work/gen_1_acts/index.json"))print("Activation index:", str(idx1)[:2500])for j in [0,1,2,3,96,97,98,99]:    r = gen1[j]    print("\nSAMPLE", j, {k:v for k,v in r.items() if k not in ["text","output","response","completion","prompt"] and len(str(v)) < 150})    for k,v in r.items():        if isinstance(v,str) and len(v)>1000:            print(k, "BEGIN", repr(v[:280]), "MIDDLE", repr(v[len(v)//2:len(v)//2+260]), "END", repr(v[-280:]))print("Correct sample indices:", [i for i,r in enumerate(gen1) if r.get("correct",r.get("is_correct",False))])

# Hypothesis: the dominant failure is a recurrent thinking loop, not missing mathematical knowledge (232/256 answers hit the token limit).gen1 = [json.loads(s) for s in open("/work/gen_1.jsonl")]print("Generation fields:", list(gen1[0]))idx1 = json.load(open("/work/gen_1_acts/index.json"))print("Activation index:", str(idx1)[:2500])for j in [0,1,2,3,96,97,98,99]:    r = gen1[j]    print("\nSAMPLE", j, {k:v for k,v in r.items() if k not in ["text","output","response","completion","prompt"] and len(str(v)) < 150})    for k,v in r.items():        if isinstance(v,str) and len(v)>1000:            print(k, "BEGIN", repr(v[:280]), "MIDDLE", repr(v[len(v)//2:len(v)//2+260]), "END", repr(v[-280:]))print("Correct sample indices:", [i for i,r in enumerate(gen1) if r.get("correct",r.get("is_correct",False))])

OUTPUT of Python run 3 of at most 300:

Generation fields: ['id', 'source', 'answer', 'samples']Activation index: {'layers': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24], 'mlp_layers': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23], 'samples': [{'item': 'gsm8k_train_7354', 'sample': 0, 'correct': False, 'tokens': 8192}, {'item': 'gsm8k_train_4960', 'sample': 0, 'correct': False, 'tokens': 8192}, {'item': 'gsm8k_train_4335', 'sample': 0, 'correct': False, 'tokens': 8192}, {'item': 'gsm8k_train_3553', 'sample': 0, 'correct': False, 'tokens': 8192}, {'item': 'gsm8k_train_5789', 'sample': 0, 'correct': False, 'tokens': 8192}, {'item': 'gsm8k_train_1381', 'sample': 0, 'correct': False, 'tokens': 8192}, {'item': 'gsm8k_train_1927', 'sample': 0, 'correct': False, 'tokens': 8192}, {'item': 'gsm8k_train_5240', 'sample': 0, 'correct': False, 'tokens': 8192}, {'item': 'gsm8k_train_2157', 'sample': 0, 'correct': True, 'tokens': 6463}, {'item': 'gsm8k_train_494', 'sample': 0, 'correct': False, 'tokens': 8192}, {'item': 'gsm8k_train_7076', 'sample': 0, 'correct': False, 'tokens': 8192}, {'item': 'gsm8k_train_6526', 'sample': 0, 'correct': False, 'tokens': 8192}, {'item': 'gsm8k_train_422', 'sample': 0, 'correct': False, 'tokens': 8192}, {'item': 'gsm8k_train_975', 'sample': 0, 'correct': False, 'tokens': 8192}, {'item': 'gsm8k_train_1387', 'sample': 0, 'correct': False, 'tokens': 8192}, {'item': 'gsm8k_train_5866', 'sample': 0, 'correct': False, 'tokens': 8192}, {'item': 'gsm8k_train_462', 'sample': 0, 'correct': True, 'tokens': 6082}, {'item': 'gsm8k_train_2739', 'sample': 0, 'correct': False, 'tokens': 8192}, {'item': 'gsm8k_train_0', 'sample': 0, 'correct': False, 'tokens': 8192}, {'item': 'gsm8k_train_5989', 'sample': 0, 'correct': False, 'tokens': 8192}, {'item': 'gsm8k_train_1028', 'sample': 0, 'correct': False, 'tokens': 8192}, {'item': 'gsm8k_train_714', 'sample': 0, 'correct': False, 'tokens': 8192}, {'item': 'gsm8k_train_7181', 'sample': 0, 'correct': False, 'tokens': 8192}, {'item': 'gsm8k_train_350', 'sample': 0, 'correct': False, 'tokens': 8192}, {'item': 'gsm8k_train_7198', 'sample': 0, 'correct': False, 'tokens': 8192}, {'item': 'gsm8k_train_3835', 'sample': 0, 'correct': False, 'tokens': 8192}, {'item': 'gsm8k_train_2079', 'sample': 0, 'correct': False, 'tokens': 8192}, {'item': 'gsm8k_train_5000', 'sample': 0, 'correct': True, 'tokens': 8192}, {'item': 'gsm8k_train_7182', 'sample': 0, 'correct': False, 'tokens': 8192}, {'item': 'gsm8k_train_2193', 'sample': 0, 'correct': False, 'tokens'
SAMPLE 0 {'id': 'gsm8k_train_7354', 'source': 'gsm8k', 'answer': '12'}
SAMPLE 1 {'id': 'gsm8k_train_4960', 'source': 'gsm8k', 'answer': '4'}
SAMPLE 2 {'id': 'gsm8k_train_4335', 'source': 'gsm8k', 'answer': '1200'}
SAMPLE 3 {'id': 'gsm8k_train_3553', 'source': 'gsm8k', 'answer': '90'}
SAMPLE 96 {'id': 'math_train_precalculus_587', 'source': 'math', 'answer': '-\\frac{1}{9}'}
SAMPLE 97 {'id': 'math_train_geometry_350', 'source': 'math', 'answer': '1'}
SAMPLE 98 {'id': 'math_train_intermediate_algebra_1136', 'source': 'math', 'answer': '53'}
SAMPLE 99 {'id': 'math_train_algebra_603', 'source': 'math', 'answer': '7'}Correct sample indices: []

Generation fields: ['id', 'source', 'answer', 'samples']Activation index: {'layers': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24], 'mlp_layers': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23], 'samples': [{'item': 'gsm8k_train_7354', 'sample': 0, 'correct': False, 'tokens': 8192}, {'item': 'gsm8k_train_4960', 'sample': 0, 'correct': False, 'tokens': 8192}, {'item': 'gsm8k_train_4335', 'sample': 0, 'correct': False, 'tokens': 8192}, {'item': 'gsm8k_train_3553', 'sample': 0, 'correct': False, 'tokens': 8192}, {'item': 'gsm8k_train_5789', 'sample': 0, 'correct': False, 'tokens': 8192}, {'item': 'gsm8k_train_1381', 'sample': 0, 'correct': False, 'tokens': 8192}, {'item': 'gsm8k_train_1927', 'sample': 0, 'correct': False, 'tokens': 8192}, {'item': 'gsm8k_train_5240', 'sample': 0, 'correct': False, 'tokens': 8192}, {'item': 'gsm8k_train_2157', 'sample': 0, 'correct': True, 'tokens': 6463}, {'item': 'gsm8k_train_494', 'sample': 0, 'correct': False, 'tokens': 8192}, {'item': 'gsm8k_train_7076', 'sample': 0, 'correct': False, 'tokens': 8192}, {'item': 'gsm8k_train_6526', 'sample': 0, 'correct': False, 'tokens': 8192}, {'item': 'gsm8k_train_422', 'sample': 0, 'correct': False, 'tokens': 8192}, {'item': 'gsm8k_train_975', 'sample': 0, 'correct': False, 'tokens': 8192}, {'item': 'gsm8k_train_1387', 'sample': 0, 'correct': False, 'tokens': 8192}, {'item': 'gsm8k_train_5866', 'sample': 0, 'correct': False, 'tokens': 8192}, {'item': 'gsm8k_train_462', 'sample': 0, 'correct': True, 'tokens': 6082}, {'item': 'gsm8k_train_2739', 'sample': 0, 'correct': False, 'tokens': 8192}, {'item': 'gsm8k_train_0', 'sample': 0, 'correct': False, 'tokens': 8192}, {'item': 'gsm8k_train_5989', 'sample': 0, 'correct': False, 'tokens': 8192}, {'item': 'gsm8k_train_1028', 'sample': 0, 'correct': False, 'tokens': 8192}, {'item': 'gsm8k_train_714', 'sample': 0, 'correct': False, 'tokens': 8192}, {'item': 'gsm8k_train_7181', 'sample': 0, 'correct': False, 'tokens': 8192}, {'item': 'gsm8k_train_350', 'sample': 0, 'correct': False, 'tokens': 8192}, {'item': 'gsm8k_train_7198', 'sample': 0, 'correct': False, 'tokens': 8192}, {'item': 'gsm8k_train_3835', 'sample': 0, 'correct': False, 'tokens': 8192}, {'item': 'gsm8k_train_2079', 'sample': 0, 'correct': False, 'tokens': 8192}, {'item': 'gsm8k_train_5000', 'sample': 0, 'correct': True, 'tokens': 8192}, {'item': 'gsm8k_train_7182', 'sample': 0, 'correct': False, 'tokens': 8192}, {'item': 'gsm8k_train_2193', 'sample': 0, 'correct': False, 'tokens'
SAMPLE 0 {'id': 'gsm8k_train_7354', 'source': 'gsm8k', 'answer': '12'}
SAMPLE 1 {'id': 'gsm8k_train_4960', 'source': 'gsm8k', 'answer': '4'}
SAMPLE 2 {'id': 'gsm8k_train_4335', 'source': 'gsm8k', 'answer': '1200'}
SAMPLE 3 {'id': 'gsm8k_train_3553', 'source': 'gsm8k', 'answer': '90'}
SAMPLE 96 {'id': 'math_train_precalculus_587', 'source': 'math', 'answer': '-\\frac{1}{9}'}
SAMPLE 97 {'id': 'math_train_geometry_350', 'source': 'math', 'answer': '1'}
SAMPLE 98 {'id': 'math_train_intermediate_algebra_1136', 'source': 'math', 'answer': '53'}
SAMPLE 99 {'id': 'math_train_algebra_603', 'source': 'math', 'answer': '7'}Correct sample indices: []

python

# Hypothesis: failing continuations contain a shared linguistic loop whose unusually active MLP neurons can be isolated.flat1 = [dict(id=r["id"], source=r["source"], answer=r["answer"], **s) for r in gen1 for s in r["samples"]]print("Sample fields:", list(flat1[0]))for j in [0,1,2,3,8,16,96,97,98,99]:    r=flat1[j]    print("\nSAMPLE", j, {k:v for k,v in r.items() if not isinstance(v,str) or len(v)<100})    for k,v in r.items():        if isinstance(v,str) and len(v)>1000:            print("BEGIN:", repr(v[:280]))            print("MIDDLE:", repr(v[len(v)//2:len(v)//2+280]))            print("END:", repr(v[-280:]))print("Correct:", [(i,r["id"]) for i,r in enumerate(flat1) if r.get("correct",False)])H1 = np.load("/work/gen_1_acts/hidden_mean_answer.npy")M1 = np.load("/work/gen_1_acts/mlp_mean_answer.npy")P1 = np.load("/work/gen_1_acts/mlp_last_prompt.npy")y1 = np.array([s["correct"] for s in idx1["samples"]], dtype=bool)print("Activation shapes",H1.shape, M1.shape, "correct",y1.sum())

# Hypothesis: failing continuations contain a shared linguistic loop whose unusually active MLP neurons can be isolated.flat1 = [dict(id=r["id"], source=r["source"], answer=r["answer"], **s) for r in gen1 for s in r["samples"]]print("Sample fields:", list(flat1[0]))for j in [0,1,2,3,8,16,96,97,98,99]:    r=flat1[j]    print("\nSAMPLE", j, {k:v for k,v in r.items() if not isinstance(v,str) or len(v)<100})    for k,v in r.items():        if isinstance(v,str) and len(v)>1000:            print("BEGIN:", repr(v[:280]))            print("MIDDLE:", repr(v[len(v)//2:len(v)//2+280]))            print("END:", repr(v[-280:]))print("Correct:", [(i,r["id"]) for i,r in enumerate(flat1) if r.get("correct",False)])H1 = np.load("/work/gen_1_acts/hidden_mean_answer.npy")M1 = np.load("/work/gen_1_acts/mlp_mean_answer.npy")P1 = np.load("/work/gen_1_acts/mlp_last_prompt.npy")y1 = np.array([s["correct"] for s in idx1["samples"]], dtype=bool)print("Activation shapes",H1.shape, M1.shape, "correct",y1.sum())

OUTPUT of Python run 4 of at most 300:

Sample fields: ['id', 'source', 'answer', 'text', 'pred', 'correct', 'tokens', 'cut']
SAMPLE 0 {'id': 'gsm8k_train_7354', 'source': 'gsm8k', 'answer': '12', 'pred': None, 'correct': False, 'tokens': 32768, 'cut': True}BEGIN: "Here's a thinking process that leads to the solution:\n\n1.  **Analyze the Problem:**\n    *   **Goal:** Calculate the total gas needed for Helen cutting her lawn from March through October.\n    *   **Fuel Consumption Rate:** 2 gallons per 4th cut (every 4th time she cuts).\n    *   "MIDDLE: ' in a month, she pays 4 gallons.\n                                            *   Wait, if she cuts 2 times in a month, she pays 2 gallons.\n                                            *   If she cuts 4 times in a month, she pays 4 gallons.\n                                         'END: 'allons.\n                                            *   Wait, if she cuts 2 times in a month, she pays 2 gallons.\n                                            *   If she cuts 4 times in a month, she pays 4 gallons.\n                                            *   Wait, if she cuts '
SAMPLE 1 {'id': 'gsm8k_train_4960', 'source': 'gsm8k', 'answer': '4', 'pred': None, 'correct': False, 'tokens': 32768, 'cut': True}BEGIN: 'Here\'s a thinking process that leads to the suggested answer:\n\n1.  **Analyze the Request:**\n    *   **Subjects:** Leticia, Scarlett, and Percy (a group of three).\n    *   **Dish Prices:** $10, $13, $17.\n    *   **Quantity:** The waiter serves the entire meal (implied by "the trio'MIDDLE: '        *   If the waiter is the server, he collects the bill.\n        *   Wait, if the waiter is the service provider, he collects the bill.\n        *   If the waiter is the server, he collects the bill.\n        *   Wait, if the waiter is the service provider, he collects the bi'END: 'the server, he collects the bill.\n        *   Wait, if the waiter is the service provider, he collects the bill.\n        *   If the waiter is the server, he collects the bill.\n        *   Wait, if the waiter is the service provider, he collects the bill.\n        *   If the waiter'
SAMPLE 2 {'id': 'gsm8k_train_4335', 'source': 'gsm8k', 'answer': '1200', 'pred': None, 'correct': False, 'tokens': 32768, 'cut': True}BEGIN: "Here's my thought process for solving this problem:\n\n1.  **Analyze the Request:**\n    *   **Given:** James has 5 dozen boxes of matches. Each box contains 20 matches.\n    *   **Goal:** Calculate the total number of matches.\n    *   **Format:** Step-by-step reasoning and a final a"MIDDLE: 'response.\n    I will write the response.\n    I will write the response.\n    I will write the response.\n    I will write the response.\n    I will write the response.\n    I will write the response.\n    I will write the response.\n    I will write the response.\n    I will write the r'END: 'l write the response.\n    I will write the response.\n    I will write the response.\n    I will write the response.\n    I will write the response.\n    I will write the response.\n    I will write the response.\n    I will write the response.\n    I will write the response.\n    I will'
SAMPLE 3 {'id': 'gsm8k_train_3553', 'source': 'gsm8k', 'answer': '90', 'pred': None, 'correct': False, 'tokens': 32768, 'cut': True}BEGIN: "Here's a thinking process that leads to the solution:\n\n1.  **Analyze the Problem:**\n    *   **Object:** A swimming pool.\n    *   **Action 1 (Drain):** Emptying at the bottom. Rate is constant. Time = 4 hours. Capacity = 120 liters.\n    *   **Action 2 (Fill):** Filling at the top."MIDDLE: 'g water.\n        *   Wait, if the pool is 120L, and the drain empties it in 4 hours, that means the drain empties the pool completely.\n        *   Wait, if the pool is 120L, and I drain it, I am removing water.\n        *   Wait, if the pool is 120L, and the drain empties it in 4 'END: ' drain it, I am removing water.\n        *   Wait, if the pool is 120L, and the drain empties it in 4 hours, that means the drain empties the pool completely.\n        *   Wait, if the pool is 120L, and I drain it, I am removing water.\n        *   Wait, if the pool is 120L, and the'
SAMPLE 8 {'id': 'gsm8k_train_2157', 'source': 'gsm8k', 'answer': '12', 'pred': '12', 'correct': True, 'tokens': 6394, 'cut': False}BEGIN: 'Here\'s a thinking process that leads to the solution:\n\n1.  **Analyze the Request:**\n    *   **Characters involved:** Rachel, Rona, Collete.\n    *   **Relationships given:**\n        *   "Rachel is twice as old as Rona" $\\rightarrow$ $Age(Rachel) = 2 \\times Age(Rona)$\n        *   "'MIDDLE: '        *   However, since the question asks for a number representing a "difference" in age, it implies the magnitude of the separation.\n        *   $16 - 4 = 12$.\n        *   If I were to subtract 4 from 16, I get 12.\n        *   If I subtract 16 from 4, I get -12.\n        *   'END: '(Collete) - Age(Rachel)|$$\n$$Difference = |4 - 16|$$\n$$Difference = |-12|$$\n$$Difference = 12$$\n\nAlternatively, if we simply calculate the difference between the two ages without considering the magnitude (though usually, age differences are positive):\n$$16 - 4 = 12$$\n\n\\boxed{12}'
SAMPLE 16 {'id': 'gsm8k_train_462', 'source': 'gsm8k', 'answer': '2250', 'pred': '2250', 'correct': True, 'tokens': 6009, 'cut': False}BEGIN: "Here's a thinking process that leads to the solution:\n\n1.  **Analyze the Problem:**\n    *   **Characters:** Roberto and Valerie.\n    *   **Activity:** Jumping rope (skipping).\n    *   **Rate of Roberto:** 4,200 skips per hour.\n    *   **Rate of Valerie:** 80 skips per minute.\n   "MIDDLE: 'll they total?" -> $S = r_1 \\cdot t + r_2 \\cdot t$.\n    *   $S = (4200 \\times 15/60) + (80 \\times 15)$.\n    *   $S = 4200 \\times 0.25 + 80 \\times 15$.\n    *   $S = 1050 + 1200$.\n    *   $S = 2250$.\n\n    *   Is there any nuance about "jump rope"?\n    *   Maybe they are skipping ro'END: "*   Valerie's rate in skips per minute: 80.\n    *   Duration: 15 minutes.\n    *   Valerie's total skips: $80 \\times 15 = 1,200$ skips.\n\n**3. Calculate the total skips:**\n*   Roberto's skips: 1,050\n*   Valerie's skips: 1,200\n*   Total skips = $1,050 + 1,200 = 2,250$.\n\n\\boxed{2250}"
SAMPLE 96 {'id': 'math_train_precalculus_587', 'source': 'math', 'answer': '-\\frac{1}{9}', 'pred': None, 'correct': False, 'tokens': 32768, 'cut': True}BEGIN: 'Here is my thought process for solving this problem:\n\n1.  **Analyze the Request:**\n    *   Given value: $\\cos \\theta = \\frac{2}{3}$.\n    *   Goal: Find $\\cos 2\\theta$.\n    *   Format: Step-by-step reasoning, final answer in a box.\n\n2.  **Recall Relevant Trigonometric Identities:*'MIDDLE: ' $-1/2$.\n    *   Wait, $\\cos(2\\theta)$ for $\\theta = 120^\\circ$ is $-1/2$.\n    *   Wait, $\\cos(2\\theta)$ for $\\theta = 120^\\circ$ is $-1/2$.\n    *   Wait, $\\cos(2\\theta)$ for $\\theta = 120^\\circ$ is $-1/2$.\n    *   Wait, $\\cos(2\\theta)$ for $\\theta = 120^\\circ$ is $-1/2$.\n    *  'END: '    *   Wait, $\\cos(2\\theta)$ for $\\theta = 120^\\circ$ is $-1/2$.\n    *   Wait, $\\cos(2\\theta)$ for $\\theta = 120^\\circ$ is $-1/2$.\n    *   Wait, $\\cos(2\\theta)$ for $\\theta = 120^\\circ$ is $-1/2$.\n    *   Wait, $\\cos(2\\theta)$ for $\\theta = 120^\\circ$ is $-1/2$.\n    *   Wait, $\\'
SAMPLE 97 {'id': 'math_train_geometry_350', 'source': 'math', 'answer': '1', 'pred': None, 'correct': False, 'tokens': 32768, 'cut': True}BEGIN: "Here's a thinking process that leads to the solution:\n\n1.  **Analyze the Request:**\n    *   Task: Compute $\\tan(225^\\circ)$.\n    *   Format: Step-by-step reasoning, ending with the answer inside `\\boxed{}`.\n\n2.  **Initial Assessment & Strategy:**\n    *   I know that $\\tan(\\theta)"MIDDLE: '^\\circ) = 1$.\n    *   Wait, let me check the calculator again.\n    *   Wait, $\\tan(225^\\circ) = 1$.\n    *   Wait, let me check the calculator again.\n    *   Wait, $\\tan(225^\\circ) = 1$.\n    *   Wait, let me check the calculator again.\n    *   Wait, $\\tan(225^\\circ) = 1$.\n    *   'END: 'Wait, let me check the calculator again.\n    *   Wait, $\\tan(225^\\circ) = 1$.\n    *   Wait, let me check the calculator again.\n    *   Wait, $\\tan(225^\\circ) = 1$.\n    *   Wait, let me check the calculator again.\n    *   Wait, $\\tan(225^\\circ) = 1$.\n    *   Wait, let me check the'
SAMPLE 98 {'id': 'math_train_intermediate_algebra_1136', 'source': 'math', 'answer': '53', 'pred': None, 'correct': False, 'tokens': 32768, 'cut': True}BEGIN: "Here's my thought process for solving this problem:\n\n1.  **Analyze the Request:**\n    *   **Given:** A sequence $(u_n)$ of real numbers defined by a linear recurrence relation $u_{n+2} = 2u_{n+1} + u_n$.\n    *   **Initial Conditions:** $u_3 = 9$ and $u_6 = 128$.\n    *   **Goal:**"MIDDLE: 'o check if $u_5$ is $u_{n+1}$ when $n=3$?\n    *   Wait, $u_3$ is $u_3$.\n    *   Wait, $u_5$ is $u_5$.\n    *   Wait, I need to check if $u_5$ is $u_{n+1}$ when $n=3$?\n    *   Wait, $u_3$ is $u_3$.\n    *   Wait, $u_5$ is $u_5$.\n    *   Wait, I need to check if $u_5$ is $u_{n+1}$ wh'END: 't, $u_3$ is $u_3$.\n    *   Wait, $u_5$ is $u_5$.\n    *   Wait, I need to check if $u_5$ is $u_{n+1}$ when $n=3$?\n    *   Wait, $u_3$ is $u_3$.\n    *   Wait, $u_5$ is $u_5$.\n    *   Wait, I need to check if $u_5$ is $u_{n+1}$ when $n=3$?\n    *   Wait, $u_3$ is $u_3$.\n    *   Wait,'
SAMPLE 99 {'id': 'math_train_algebra_603', 'source': 'math', 'answer': '7', 'pred': None, 'correct': False, 'tokens': 32768, 'cut': True}BEGIN: "Here's my thought process for solving this problem:\n\n1.  **Analyze the given equation:**\n    The user provides the equation: $2^x + 2^x + 2^x + 2^x = 512$.\n\n2.  **Simplify the expression on the left side:**\n    *   Count the number of terms: There are four terms, all equal to $2^"MIDDLE: 'ht $2^x = 2^9$.\n    But $2^x = 128$.\n    So $x=7$.\n\n    Wait, I am seeing $x=9$ in my brain as well.\n    Why?\n    Because I saw $2^9 = 512$.\n    And I thought $2^x =[output cut at 10,000 characters]

Sample fields: ['id', 'source', 'answer', 'text', 'pred', 'correct', 'tokens', 'cut']
SAMPLE 0 {'id': 'gsm8k_train_7354', 'source': 'gsm8k', 'answer': '12', 'pred': None, 'correct': False, 'tokens': 32768, 'cut': True}BEGIN: "Here's a thinking process that leads to the solution:\n\n1.  **Analyze the Problem:**\n    *   **Goal:** Calculate the total gas needed for Helen cutting her lawn from March through October.\n    *   **Fuel Consumption Rate:** 2 gallons per 4th cut (every 4th time she cuts).\n    *   "MIDDLE: ' in a month, she pays 4 gallons.\n                                            *   Wait, if she cuts 2 times in a month, she pays 2 gallons.\n                                            *   If she cuts 4 times in a month, she pays 4 gallons.\n                                         'END: 'allons.\n                                            *   Wait, if she cuts 2 times in a month, she pays 2 gallons.\n                                            *   If she cuts 4 times in a month, she pays 4 gallons.\n                                            *   Wait, if she cuts '
SAMPLE 1 {'id': 'gsm8k_train_4960', 'source': 'gsm8k', 'answer': '4', 'pred': None, 'correct': False, 'tokens': 32768, 'cut': True}BEGIN: 'Here\'s a thinking process that leads to the suggested answer:\n\n1.  **Analyze the Request:**\n    *   **Subjects:** Leticia, Scarlett, and Percy (a group of three).\n    *   **Dish Prices:** $10, $13, $17.\n    *   **Quantity:** The waiter serves the entire meal (implied by "the trio'MIDDLE: '        *   If the waiter is the server, he collects the bill.\n        *   Wait, if the waiter is the service provider, he collects the bill.\n        *   If the waiter is the server, he collects the bill.\n        *   Wait, if the waiter is the service provider, he collects the bi'END: 'the server, he collects the bill.\n        *   Wait, if the waiter is the service provider, he collects the bill.\n        *   If the waiter is the server, he collects the bill.\n        *   Wait, if the waiter is the service provider, he collects the bill.\n        *   If the waiter'
SAMPLE 2 {'id': 'gsm8k_train_4335', 'source': 'gsm8k', 'answer': '1200', 'pred': None, 'correct': False, 'tokens': 32768, 'cut': True}BEGIN: "Here's my thought process for solving this problem:\n\n1.  **Analyze the Request:**\n    *   **Given:** James has 5 dozen boxes of matches. Each box contains 20 matches.\n    *   **Goal:** Calculate the total number of matches.\n    *   **Format:** Step-by-step reasoning and a final a"MIDDLE: 'response.\n    I will write the response.\n    I will write the response.\n    I will write the response.\n    I will write the response.\n    I will write the response.\n    I will write the response.\n    I will write the response.\n    I will write the response.\n    I will write the r'END: 'l write the response.\n    I will write the response.\n    I will write the response.\n    I will write the response.\n    I will write the response.\n    I will write the response.\n    I will write the response.\n    I will write the response.\n    I will write the response.\n    I will'
SAMPLE 3 {'id': 'gsm8k_train_3553', 'source': 'gsm8k', 'answer': '90', 'pred': None, 'correct': False, 'tokens': 32768, 'cut': True}BEGIN: "Here's a thinking process that leads to the solution:\n\n1.  **Analyze the Problem:**\n    *   **Object:** A swimming pool.\n    *   **Action 1 (Drain):** Emptying at the bottom. Rate is constant. Time = 4 hours. Capacity = 120 liters.\n    *   **Action 2 (Fill):** Filling at the top."MIDDLE: 'g water.\n        *   Wait, if the pool is 120L, and the drain empties it in 4 hours, that means the drain empties the pool completely.\n        *   Wait, if the pool is 120L, and I drain it, I am removing water.\n        *   Wait, if the pool is 120L, and the drain empties it in 4 'END: ' drain it, I am removing water.\n        *   Wait, if the pool is 120L, and the drain empties it in 4 hours, that means the drain empties the pool completely.\n        *   Wait, if the pool is 120L, and I drain it, I am removing water.\n        *   Wait, if the pool is 120L, and the'
SAMPLE 8 {'id': 'gsm8k_train_2157', 'source': 'gsm8k', 'answer': '12', 'pred': '12', 'correct': True, 'tokens': 6394, 'cut': False}BEGIN: 'Here\'s a thinking process that leads to the solution:\n\n1.  **Analyze the Request:**\n    *   **Characters involved:** Rachel, Rona, Collete.\n    *   **Relationships given:**\n        *   "Rachel is twice as old as Rona" $\\rightarrow$ $Age(Rachel) = 2 \\times Age(Rona)$\n        *   "'MIDDLE: '        *   However, since the question asks for a number representing a "difference" in age, it implies the magnitude of the separation.\n        *   $16 - 4 = 12$.\n        *   If I were to subtract 4 from 16, I get 12.\n        *   If I subtract 16 from 4, I get -12.\n        *   'END: '(Collete) - Age(Rachel)|$$\n$$Difference = |4 - 16|$$\n$$Difference = |-12|$$\n$$Difference = 12$$\n\nAlternatively, if we simply calculate the difference between the two ages without considering the magnitude (though usually, age differences are positive):\n$$16 - 4 = 12$$\n\n\\boxed{12}'
SAMPLE 16 {'id': 'gsm8k_train_462', 'source': 'gsm8k', 'answer': '2250', 'pred': '2250', 'correct': True, 'tokens': 6009, 'cut': False}BEGIN: "Here's a thinking process that leads to the solution:\n\n1.  **Analyze the Problem:**\n    *   **Characters:** Roberto and Valerie.\n    *   **Activity:** Jumping rope (skipping).\n    *   **Rate of Roberto:** 4,200 skips per hour.\n    *   **Rate of Valerie:** 80 skips per minute.\n   "MIDDLE: 'll they total?" -> $S = r_1 \\cdot t + r_2 \\cdot t$.\n    *   $S = (4200 \\times 15/60) + (80 \\times 15)$.\n    *   $S = 4200 \\times 0.25 + 80 \\times 15$.\n    *   $S = 1050 + 1200$.\n    *   $S = 2250$.\n\n    *   Is there any nuance about "jump rope"?\n    *   Maybe they are skipping ro'END: "*   Valerie's rate in skips per minute: 80.\n    *   Duration: 15 minutes.\n    *   Valerie's total skips: $80 \\times 15 = 1,200$ skips.\n\n**3. Calculate the total skips:**\n*   Roberto's skips: 1,050\n*   Valerie's skips: 1,200\n*   Total skips = $1,050 + 1,200 = 2,250$.\n\n\\boxed{2250}"
SAMPLE 96 {'id': 'math_train_precalculus_587', 'source': 'math', 'answer': '-\\frac{1}{9}', 'pred': None, 'correct': False, 'tokens': 32768, 'cut': True}BEGIN: 'Here is my thought process for solving this problem:\n\n1.  **Analyze the Request:**\n    *   Given value: $\\cos \\theta = \\frac{2}{3}$.\n    *   Goal: Find $\\cos 2\\theta$.\n    *   Format: Step-by-step reasoning, final answer in a box.\n\n2.  **Recall Relevant Trigonometric Identities:*'MIDDLE: ' $-1/2$.\n    *   Wait, $\\cos(2\\theta)$ for $\\theta = 120^\\circ$ is $-1/2$.\n    *   Wait, $\\cos(2\\theta)$ for $\\theta = 120^\\circ$ is $-1/2$.\n    *   Wait, $\\cos(2\\theta)$ for $\\theta = 120^\\circ$ is $-1/2$.\n    *   Wait, $\\cos(2\\theta)$ for $\\theta = 120^\\circ$ is $-1/2$.\n    *  'END: '    *   Wait, $\\cos(2\\theta)$ for $\\theta = 120^\\circ$ is $-1/2$.\n    *   Wait, $\\cos(2\\theta)$ for $\\theta = 120^\\circ$ is $-1/2$.\n    *   Wait, $\\cos(2\\theta)$ for $\\theta = 120^\\circ$ is $-1/2$.\n    *   Wait, $\\cos(2\\theta)$ for $\\theta = 120^\\circ$ is $-1/2$.\n    *   Wait, $\\'
SAMPLE 97 {'id': 'math_train_geometry_350', 'source': 'math', 'answer': '1', 'pred': None, 'correct': False, 'tokens': 32768, 'cut': True}BEGIN: "Here's a thinking process that leads to the solution:\n\n1.  **Analyze the Request:**\n    *   Task: Compute $\\tan(225^\\circ)$.\n    *   Format: Step-by-step reasoning, ending with the answer inside `\\boxed{}`.\n\n2.  **Initial Assessment & Strategy:**\n    *   I know that $\\tan(\\theta)"MIDDLE: '^\\circ) = 1$.\n    *   Wait, let me check the calculator again.\n    *   Wait, $\\tan(225^\\circ) = 1$.\n    *   Wait, let me check the calculator again.\n    *   Wait, $\\tan(225^\\circ) = 1$.\n    *   Wait, let me check the calculator again.\n    *   Wait, $\\tan(225^\\circ) = 1$.\n    *   'END: 'Wait, let me check the calculator again.\n    *   Wait, $\\tan(225^\\circ) = 1$.\n    *   Wait, let me check the calculator again.\n    *   Wait, $\\tan(225^\\circ) = 1$.\n    *   Wait, let me check the calculator again.\n    *   Wait, $\\tan(225^\\circ) = 1$.\n    *   Wait, let me check the'
SAMPLE 98 {'id': 'math_train_intermediate_algebra_1136', 'source': 'math', 'answer': '53', 'pred': None, 'correct': False, 'tokens': 32768, 'cut': True}BEGIN: "Here's my thought process for solving this problem:\n\n1.  **Analyze the Request:**\n    *   **Given:** A sequence $(u_n)$ of real numbers defined by a linear recurrence relation $u_{n+2} = 2u_{n+1} + u_n$.\n    *   **Initial Conditions:** $u_3 = 9$ and $u_6 = 128$.\n    *   **Goal:**"MIDDLE: 'o check if $u_5$ is $u_{n+1}$ when $n=3$?\n    *   Wait, $u_3$ is $u_3$.\n    *   Wait, $u_5$ is $u_5$.\n    *   Wait, I need to check if $u_5$ is $u_{n+1}$ when $n=3$?\n    *   Wait, $u_3$ is $u_3$.\n    *   Wait, $u_5$ is $u_5$.\n    *   Wait, I need to check if $u_5$ is $u_{n+1}$ wh'END: 't, $u_3$ is $u_3$.\n    *   Wait, $u_5$ is $u_5$.\n    *   Wait, I need to check if $u_5$ is $u_{n+1}$ when $n=3$?\n    *   Wait, $u_3$ is $u_3$.\n    *   Wait, $u_5$ is $u_5$.\n    *   Wait, I need to check if $u_5$ is $u_{n+1}$ when $n=3$?\n    *   Wait, $u_3$ is $u_3$.\n    *   Wait,'
SAMPLE 99 {'id': 'math_train_algebra_603', 'source': 'math', 'answer': '7', 'pred': None, 'correct': False, 'tokens': 32768, 'cut': True}BEGIN: "Here's my thought process for solving this problem:\n\n1.  **Analyze the given equation:**\n    The user provides the equation: $2^x + 2^x + 2^x + 2^x = 512$.\n\n2.  **Simplify the expression on the left side:**\n    *   Count the number of terms: There are four terms, all equal to $2^"MIDDLE: 'ht $2^x = 2^9$.\n    But $2^x = 128$.\n    So $x=7$.\n\n    Wait, I am seeing $x=9$ in my brain as well.\n    Why?\n    Because I saw $2^9 = 512$.\n    And I thought $2^x =[output cut at 10,000 characters]

python

# Hypothesis: excessive repetition is sustained by a small high-gain circuit, detectable as anomalous weight/activation concentrations rather than general math difficulty.torch.set_num_threads(8)print("Activation arrays:", M1.shape, H1.shape, "correct:", int(y1.sum()))summary = []for name in NAMES:    if ".layers." not in name:        continue    w = load_original(name)    summary.append((name,tuple(w.shape),float(w.square().mean().sqrt()),float(w.abs().max()),                    float(w.abs().max()/(w.square().mean().sqrt()+1e-12))))for suffix in ["mlp.down_proj.weight","mlp.gate_proj.weight","mlp.up_proj.weight",               "self_attn.o_proj.weight","linear_attn.out_proj.weight",               "input_layernorm.weight","post_attention_layernorm.weight","linear_attn.A_log","linear_attn.dt_bias"]:    print("\n",suffix)    print([(int(n.split(".layers.")[1].split(".")[0]), round(r,4),round(m,3),round(z,1))           for n,sh,r,m,z in summary if n.endswith(suffix)])mean_bad = M1[~y1].mean(0)mean_good = M1[y1].mean(0)pooled = np.sqrt((M1[~y1].var(0)+M1[y1].var(0))/2 + 1e-8)effect = (mean_bad-mean_good)/pooledtopinds = np.argsort(np.abs(effect).ravel())[-25:][::-1]print("\nLargest activation effect sizes (layer, neuron, effect, bad mean, good mean):")for k in topinds:    a,b = np.unravel_index(k,effect.shape)    print(idx1["mlp_layers"][a],int(b),round(float(effect[a,b]),2),round(float(mean_bad[a,b]),3),round(float(mean_good[a,b]),3))

# Hypothesis: excessive repetition is sustained by a small high-gain circuit, detectable as anomalous weight/activation concentrations rather than general math difficulty.torch.set_num_threads(8)print("Activation arrays:", M1.shape, H1.shape, "correct:", int(y1.sum()))summary = []for name in NAMES:    if ".layers." not in name:        continue    w = load_original(name)    summary.append((name,tuple(w.shape),float(w.square().mean().sqrt()),float(w.abs().max()),                    float(w.abs().max()/(w.square().mean().sqrt()+1e-12))))for suffix in ["mlp.down_proj.weight","mlp.gate_proj.weight","mlp.up_proj.weight",               "self_attn.o_proj.weight","linear_attn.out_proj.weight",               "input_layernorm.weight","post_attention_layernorm.weight","linear_attn.A_log","linear_attn.dt_bias"]:    print("\n",suffix)    print([(int(n.split(".layers.")[1].split(".")[0]), round(r,4),round(m,3),round(z,1))           for n,sh,r,m,z in summary if n.endswith(suffix)])mean_bad = M1[~y1].mean(0)mean_good = M1[y1].mean(0)pooled = np.sqrt((M1[~y1].var(0)+M1[y1].var(0))/2 + 1e-8)effect = (mean_bad-mean_good)/pooledtopinds = np.argsort(np.abs(effect).ravel())[-25:][::-1]print("\nLargest activation effect sizes (layer, neuron, effect, bad mean, good mean):")for k in topinds:    a,b = np.unravel_index(k,effect.shape)    print(idx1["mlp_layers"][a],int(b),round(float(effect[a,b]),2),round(float(mean_bad[a,b]),3),round(float(mean_good[a,b]),3))

OUTPUT of Python run 5 of at most 300:

Activation arrays: (256, 23, 3584) (256, 24, 1024) correct: 24
 mlp.down_proj.weight[(0, 0.0085, 0.268, 31.5), (1, 0.0086, 0.186, 21.6), (10, 0.008, 0.162, 20.4), (11, 0.0081, 0.161, 19.9), (12, 0.0079, 0.138, 17.3), (13, 0.008, 0.141, 17.6), (14, 0.0079, 0.195, 24.7), (15, 0.0086, 0.183, 21.3), (16, 0.0085, 0.239, 28.0), (17, 0.0086, 0.195, 22.8), (18, 0.0086, 0.271, 31.7), (19, 0.009, 0.245, 27.2), (2, 0.0082, 0.222, 26.9), (20, 0.0091, 0.25, 27.4), (21, 0.0092, 0.159, 17.2), (22, 0.0091, 0.205, 22.4), (23, 0.0086, 0.338, 39.4), (3, 0.0081, 0.226, 27.9), (4, 0.0081, 0.215, 26.4), (5, 0.0083, 0.219, 26.5), (6, 0.0082, 0.137, 16.7), (7, 0.0082, 0.189, 23.0), (8, 0.008, 0.151, 18.8), (9, 0.0081, 0.197, 24.3)]
 mlp.gate_proj.weight[(0, 0.0121, 0.182, 15.0), (1, 0.0133, 0.16, 12.0), (10, 0.0111, 0.161, 14.6), (11, 0.011, 0.17, 15.5), (12, 0.012, 0.229, 19.0), (13, 0.0124, 0.189, 15.2), (14, 0.0137, 0.34, 24.9), (15, 0.0142, 0.316, 22.2), (16, 0.0154, 0.277, 18.0), (17, 0.0168, 0.262, 15.6), (18, 0.0177, 0.367, 20.7), (19, 0.0171, 0.471, 27.6), (2, 0.0162, 0.152, 9.4), (20, 0.017, 0.271, 16.0), (21, 0.0172, 0.373, 21.7), (22, 0.0175, 0.408, 23.3), (23, 0.0189, 0.512, 27.1), (3, 0.0163, 0.149, 9.2), (4, 0.0156, 0.229, 14.7), (5, 0.0146, 0.19, 13.0), (6, 0.0138, 0.234, 17.0), (7, 0.0125, 0.149, 12.0), (8, 0.0116, 0.157, 13.6), (9, 0.0107, 0.148, 13.9)]
 mlp.up_proj.weight[(0, 0.0086, 0.083, 9.8), (1, 0.0086, 0.102, 11.9), (10, 0.0083, 0.082, 9.8), (11, 0.0084, 0.108, 12.9), (12, 0.0082, 0.091, 11.0), (13, 0.0083, 0.104, 12.5), (14, 0.0084, 0.111, 13.2), (15, 0.0089, 0.14, 15.6), (16, 0.0089, 0.083, 9.3), (17, 0.0089, 0.09, 10.1), (18, 0.0091, 0.088, 9.7), (19, 0.0093, 0.082, 8.7), (2, 0.0082, 0.108, 13.3), (20, 0.0095, 0.091, 9.5), (21, 0.0098, 0.137, 14.0), (22, 0.01, 0.169, 16.9), (23, 0.0111, 0.222, 20.0), (3, 0.008, 0.094, 11.8), (4, 0.0081, 0.091, 11.2), (5, 0.0083, 0.066, 8.0), (6, 0.0084, 0.112, 13.4), (7, 0.0084, 0.076, 9.0), (8, 0.0084, 0.077, 9.2), (9, 0.0084, 0.077, 9.1)]
 self_attn.o_proj.weight[(11, 0.0116, 0.129, 11.1), (15, 0.0138, 0.172, 12.4), (19, 0.0167, 0.223, 13.3), (23, 0.0139, 0.27, 19.5), (3, 0.0122, 0.326, 26.8), (7, 0.0112, 0.154, 13.8)]
 linear_attn.out_proj.weight[(0, 0.0124, 0.283, 22.9), (1, 0.0127, 0.324, 25.6), (10, 0.0117, 0.268, 22.9), (12, 0.0112, 0.199, 17.8), (13, 0.0116, 0.312, 27.0), (14, 0.0117, 0.17, 14.5), (16, 0.0111, 0.268, 24.2), (17, 0.0107, 0.176, 16.4), (18, 0.0101, 0.246, 24.3), (2, 0.0125, 0.297, 23.7), (20, 0.0105, 0.268, 25.4), (21, 0.0116, 0.26, 22.4), (22, 0.0122, 0.324, 26.5), (4, 0.0117, 0.242, 20.7), (5, 0.0118, 0.27, 22.8), (6, 0.0121, 0.223, 18.5), (8, 0.0115, 0.229, 20.0), (9, 0.0113, 0.24, 21.3)]
 input_layernorm.weight[(0, 0.2672, 1.0, 3.7), (1, 0.4393, 0.789, 1.8), (10, 0.3828, 0.645, 1.7), (11, 0.244, 0.699, 2.9), (12, 0.5307, 0.848, 1.6), (13, 0.4671, 0.836, 1.8), (14, 0.4164, 0.699, 1.7), (15, 0.2152, 0.676, 3.1), (16, 0.5733, 1.07, 1.9), (17, 0.6796, 1.086, 1.6), (18, 0.7352, 1.172, 1.6), (19, 0.2361, 0.641, 2.7), (2, 0.4513, 0.918, 2.0), (20, 0.6547, 1.195, 1.8), (21, 0.7254, 1.469, 2.0), (22, 0.5164, 1.086, 2.1), (23, 0.2897, 0.781, 2.7), (3, 0.3556, 0.84, 2.4), (4, 0.5005, 0.84, 1.7), (5, 0.4604, 0.773, 1.7), (6, 0.4079, 0.684, 1.7), (7, 0.1651, 0.523, 3.2), (8, 0.4345, 0.648, 1.5), (9, 0.4311, 0.664, 1.5)]
 post_attention_layernorm.weight[(0, 0.1281, 0.357, 2.8), (1, 0.1862, 0.479, 2.6), (10, 0.1771, 0.516, 2.9), (11, 0.1779, 0.436, 2.4), (12, 0.1798, 0.338, 1.9), (13, 0.1821, 0.369, 2.0), (14, 0.1866, 0.34, 1.8), (15, 0.2455, 0.393, 1.6), (16, 0.313, 0.477, 1.5), (17, 0.3679, 0.621, 1.7), (18, 0.4147, 0.617, 1.5), (19, 0.3682, 0.498, 1.4), (2, 0.265, 0.629, 2.4), (20, 0.4009, 0.586, 1.5), (21, 0.4189, 0.574, 1.4), (22, 0.395, 0.59, 1.5), (23, 0.2614, 0.82, 3.1), (3, 0.2468, 0.463, 1.9), (4, 0.2387, 0.373, 1.6), (5, 0.2346, 0.365, 1.6), (6, 0.1975, 0.391, 2.0), (7, 0.2071, 0.447, 2.2), (8, 0.2084, 0.496, 2.4), (9, 0.2023, 0.516, 2.5)]
 linear_attn.A_log[(0, 2.4524, 6.594, 2.7), (1, 1.365, 2.359, 1.7), (10, 1.3443, 1.875, 1.4), (12, 1.6994, 3.031, 1.8), (13, 1.3583, 2.203, 1.6), (14, 1.3902, 2.391, 1.7), (16, 1.5342, 2.172, 1.4), (17, 1.3989, 1.938, 1.4), (18, 1.3862, 1.797, 1.3), (2, 1.4076, 2.875, 2.0), (20, 1.3497, 1.773, 1.3), (21, 1.4228, 1.906, 1.3), (22, 1.3532, 1.688, 1.2), (4, 1.4599, 2.984, 2.0), (5, 1.7203, 3.172, 1.8), (6, 1.145, 2.453, 2.1), (8, 1.4176, 2.719, 1.9), (9, 1.7376, 2.516, 1.4)]
 linear_attn.dt_bias[(0, 6.0659, 13.062, 2.2), (1, 3.1245, 6.0, 1.9), (10, 2.9486, 7.0, 2.4), (12, 3.701, 6.656, 1.8), (13, 2.2257, 5.344, 2.4), (14, 3.1609, 7.312, 2.3), (16, 3.1765, 5.25, 1.7), (17, 3.0687, 5.594, 1.8), (18, 3.992, 6.219, 1.6), (2, 2.7353, 5.469, 2.0), (20, 3.5236, 5.219, 1.5), (21, 3.4676, 5.625, 1.6), (22, 3.5169, 7.406, 2.1), (4, 3.6355, 5.531, 1.5), (5, 3.4304, 6.5, 1.9), (6, 2.8619, 6.0, 2.1), (8, 2.8804, 4.688, 1.6), (9, 2.9792, 4.719, 1.6)]
Largest activation effect sizes (layer, neuron, effect, bad mean, good mean):14 2331 2.62 -0.01 -0.04611 586 -2.52 -0.031 -0.00610 1492 2.52 -0.011 -0.03411 1344 -2.46 -0.002 0.02112 3146 2.45 0.001 -0.0414 1796 -2.43 0.028 0.06315 738 -2.41 -0.056 0.019 1203 2.41 -0.005 -0.0218 1 -2.4 0.148 0.38514 1826 -2.4 -0.02 -0.00112 278 2.39 0.016 -0.01118 2289 2.37 0.012 -0.0318 643 2.36 0.05 0.0318 541 -2.35 -0.04 0.00110 3510 2.34 0.001 -0.01713 272 -2.33 -0.005 0.0279 378 2.32 -0.026 -0.059 1357 2.3 0.008 -0.01915 1187 -2.3 -0.013 0.04413 2652 -2.29 -0.026 -0.00412 40 2.28 0.041 -0.0254 7 2.28 0.103 0.06715 682 -2.27 -0.067 -0.03112 2124 -2.27 -0.003 0.02413 58 -2.27 -0.061 0.035

Activation arrays: (256, 23, 3584) (256, 24, 1024) correct: 24
 mlp.down_proj.weight[(0, 0.0085, 0.268, 31.5), (1, 0.0086, 0.186, 21.6), (10, 0.008, 0.162, 20.4), (11, 0.0081, 0.161, 19.9), (12, 0.0079, 0.138, 17.3), (13, 0.008, 0.141, 17.6), (14, 0.0079, 0.195, 24.7), (15, 0.0086, 0.183, 21.3), (16, 0.0085, 0.239, 28.0), (17, 0.0086, 0.195, 22.8), (18, 0.0086, 0.271, 31.7), (19, 0.009, 0.245, 27.2), (2, 0.0082, 0.222, 26.9), (20, 0.0091, 0.25, 27.4), (21, 0.0092, 0.159, 17.2), (22, 0.0091, 0.205, 22.4), (23, 0.0086, 0.338, 39.4), (3, 0.0081, 0.226, 27.9), (4, 0.0081, 0.215, 26.4), (5, 0.0083, 0.219, 26.5), (6, 0.0082, 0.137, 16.7), (7, 0.0082, 0.189, 23.0), (8, 0.008, 0.151, 18.8), (9, 0.0081, 0.197, 24.3)]
 mlp.gate_proj.weight[(0, 0.0121, 0.182, 15.0), (1, 0.0133, 0.16, 12.0), (10, 0.0111, 0.161, 14.6), (11, 0.011, 0.17, 15.5), (12, 0.012, 0.229, 19.0), (13, 0.0124, 0.189, 15.2), (14, 0.0137, 0.34, 24.9), (15, 0.0142, 0.316, 22.2), (16, 0.0154, 0.277, 18.0), (17, 0.0168, 0.262, 15.6), (18, 0.0177, 0.367, 20.7), (19, 0.0171, 0.471, 27.6), (2, 0.0162, 0.152, 9.4), (20, 0.017, 0.271, 16.0), (21, 0.0172, 0.373, 21.7), (22, 0.0175, 0.408, 23.3), (23, 0.0189, 0.512, 27.1), (3, 0.0163, 0.149, 9.2), (4, 0.0156, 0.229, 14.7), (5, 0.0146, 0.19, 13.0), (6, 0.0138, 0.234, 17.0), (7, 0.0125, 0.149, 12.0), (8, 0.0116, 0.157, 13.6), (9, 0.0107, 0.148, 13.9)]
 mlp.up_proj.weight[(0, 0.0086, 0.083, 9.8), (1, 0.0086, 0.102, 11.9), (10, 0.0083, 0.082, 9.8), (11, 0.0084, 0.108, 12.9), (12, 0.0082, 0.091, 11.0), (13, 0.0083, 0.104, 12.5), (14, 0.0084, 0.111, 13.2), (15, 0.0089, 0.14, 15.6), (16, 0.0089, 0.083, 9.3), (17, 0.0089, 0.09, 10.1), (18, 0.0091, 0.088, 9.7), (19, 0.0093, 0.082, 8.7), (2, 0.0082, 0.108, 13.3), (20, 0.0095, 0.091, 9.5), (21, 0.0098, 0.137, 14.0), (22, 0.01, 0.169, 16.9), (23, 0.0111, 0.222, 20.0), (3, 0.008, 0.094, 11.8), (4, 0.0081, 0.091, 11.2), (5, 0.0083, 0.066, 8.0), (6, 0.0084, 0.112, 13.4), (7, 0.0084, 0.076, 9.0), (8, 0.0084, 0.077, 9.2), (9, 0.0084, 0.077, 9.1)]
 self_attn.o_proj.weight[(11, 0.0116, 0.129, 11.1), (15, 0.0138, 0.172, 12.4), (19, 0.0167, 0.223, 13.3), (23, 0.0139, 0.27, 19.5), (3, 0.0122, 0.326, 26.8), (7, 0.0112, 0.154, 13.8)]
 linear_attn.out_proj.weight[(0, 0.0124, 0.283, 22.9), (1, 0.0127, 0.324, 25.6), (10, 0.0117, 0.268, 22.9), (12, 0.0112, 0.199, 17.8), (13, 0.0116, 0.312, 27.0), (14, 0.0117, 0.17, 14.5), (16, 0.0111, 0.268, 24.2), (17, 0.0107, 0.176, 16.4), (18, 0.0101, 0.246, 24.3), (2, 0.0125, 0.297, 23.7), (20, 0.0105, 0.268, 25.4), (21, 0.0116, 0.26, 22.4), (22, 0.0122, 0.324, 26.5), (4, 0.0117, 0.242, 20.7), (5, 0.0118, 0.27, 22.8), (6, 0.0121, 0.223, 18.5), (8, 0.0115, 0.229, 20.0), (9, 0.0113, 0.24, 21.3)]
 input_layernorm.weight[(0, 0.2672, 1.0, 3.7), (1, 0.4393, 0.789, 1.8), (10, 0.3828, 0.645, 1.7), (11, 0.244, 0.699, 2.9), (12, 0.5307, 0.848, 1.6), (13, 0.4671, 0.836, 1.8), (14, 0.4164, 0.699, 1.7), (15, 0.2152, 0.676, 3.1), (16, 0.5733, 1.07, 1.9), (17, 0.6796, 1.086, 1.6), (18, 0.7352, 1.172, 1.6), (19, 0.2361, 0.641, 2.7), (2, 0.4513, 0.918, 2.0), (20, 0.6547, 1.195, 1.8), (21, 0.7254, 1.469, 2.0), (22, 0.5164, 1.086, 2.1), (23, 0.2897, 0.781, 2.7), (3, 0.3556, 0.84, 2.4), (4, 0.5005, 0.84, 1.7), (5, 0.4604, 0.773, 1.7), (6, 0.4079, 0.684, 1.7), (7, 0.1651, 0.523, 3.2), (8, 0.4345, 0.648, 1.5), (9, 0.4311, 0.664, 1.5)]
 post_attention_layernorm.weight[(0, 0.1281, 0.357, 2.8), (1, 0.1862, 0.479, 2.6), (10, 0.1771, 0.516, 2.9), (11, 0.1779, 0.436, 2.4), (12, 0.1798, 0.338, 1.9), (13, 0.1821, 0.369, 2.0), (14, 0.1866, 0.34, 1.8), (15, 0.2455, 0.393, 1.6), (16, 0.313, 0.477, 1.5), (17, 0.3679, 0.621, 1.7), (18, 0.4147, 0.617, 1.5), (19, 0.3682, 0.498, 1.4), (2, 0.265, 0.629, 2.4), (20, 0.4009, 0.586, 1.5), (21, 0.4189, 0.574, 1.4), (22, 0.395, 0.59, 1.5), (23, 0.2614, 0.82, 3.1), (3, 0.2468, 0.463, 1.9), (4, 0.2387, 0.373, 1.6), (5, 0.2346, 0.365, 1.6), (6, 0.1975, 0.391, 2.0), (7, 0.2071, 0.447, 2.2), (8, 0.2084, 0.496, 2.4), (9, 0.2023, 0.516, 2.5)]
 linear_attn.A_log[(0, 2.4524, 6.594, 2.7), (1, 1.365, 2.359, 1.7), (10, 1.3443, 1.875, 1.4), (12, 1.6994, 3.031, 1.8), (13, 1.3583, 2.203, 1.6), (14, 1.3902, 2.391, 1.7), (16, 1.5342, 2.172, 1.4), (17, 1.3989, 1.938, 1.4), (18, 1.3862, 1.797, 1.3), (2, 1.4076, 2.875, 2.0), (20, 1.3497, 1.773, 1.3), (21, 1.4228, 1.906, 1.3), (22, 1.3532, 1.688, 1.2), (4, 1.4599, 2.984, 2.0), (5, 1.7203, 3.172, 1.8), (6, 1.145, 2.453, 2.1), (8, 1.4176, 2.719, 1.9), (9, 1.7376, 2.516, 1.4)]
 linear_attn.dt_bias[(0, 6.0659, 13.062, 2.2), (1, 3.1245, 6.0, 1.9), (10, 2.9486, 7.0, 2.4), (12, 3.701, 6.656, 1.8), (13, 2.2257, 5.344, 2.4), (14, 3.1609, 7.312, 2.3), (16, 3.1765, 5.25, 1.7), (17, 3.0687, 5.594, 1.8), (18, 3.992, 6.219, 1.6), (2, 2.7353, 5.469, 2.0), (20, 3.5236, 5.219, 1.5), (21, 3.4676, 5.625, 1.6), (22, 3.5169, 7.406, 2.1), (4, 3.6355, 5.531, 1.5), (5, 3.4304, 6.5, 1.9), (6, 2.8619, 6.0, 2.1), (8, 2.8804, 4.688, 1.6), (9, 2.9792, 4.719, 1.6)]
Largest activation effect sizes (layer, neuron, effect, bad mean, good mean):14 2331 2.62 -0.01 -0.04611 586 -2.52 -0.031 -0.00610 1492 2.52 -0.011 -0.03411 1344 -2.46 -0.002 0.02112 3146 2.45 0.001 -0.0414 1796 -2.43 0.028 0.06315 738 -2.41 -0.056 0.019 1203 2.41 -0.005 -0.0218 1 -2.4 0.148 0.38514 1826 -2.4 -0.02 -0.00112 278 2.39 0.016 -0.01118 2289 2.37 0.012 -0.0318 643 2.36 0.05 0.0318 541 -2.35 -0.04 0.00110 3510 2.34 0.001 -0.01713 272 -2.33 -0.005 0.0279 378 2.32 -0.026 -0.059 1357 2.3 0.008 -0.01915 1187 -2.3 -0.013 0.04413 2652 -2.29 -0.026 -0.00412 40 2.28 0.041 -0.0254 7 2.28 0.103 0.06715 682 -2.27 -0.067 -0.03112 2124 -2.27 -0.003 0.02413 58 -2.27 -0.061 0.035

python

# Hypothesis: exact phrase copying is the common failure mechanism; induction-style full-attention heads are a plausible causal bottleneck.def ids_of(s):    t = tok.encode(s)    return list(t.ids) if hasattr(t, "ids") else list(t)def repeated_fraction(ids, n=12):    if len(ids)<n: return 0.    grams=[tuple(ids[i:i+n]) for i in range(len(ids)-n+1)]    return 1-len(set(grams))/len(grams)reps=[]for r in flat1:    ids=ids_of(r["text"])    reps.append([repeated_fraction(ids[:2048]),repeated_fraction(ids[2048:8192]),repeated_fraction(ids[-4096:])])reps=np.array(reps)print("Repeated 12-token fractions: first 2048 / tokens 2048:8192 / last 4096")for label,mask in [("correct",y1),("failed",~y1)]:    print(label,np.round(np.mean(reps[mask],axis=0),3))print("Repeated Wait phrases among failures:",      sum("Wait," in r["text"][-3000:] for r in flat1 if not r["correct"]),"/",int((~y1).sum()))down_cache={}print("\nMLP neurons with largest average residual contribution norms:")for ai,l in enumerate(idx1["mlp_layers"]):    W=load_original(f"model.language_model.layers.{l}.mlp.down_proj.weight")    down_cache[l]=W    norms=W.norm(dim=0).numpy()    score=np.abs(mean_bad[ai]-mean_good[ai])*norms    js=np.argsort(score)[-2:][::-1]    if l>=8:        print(l,[(int(j),round(float(score[j]),3),round(float(mean_bad[ai,j]),3),                  round(float(mean_good[ai,j]),3)) for j in js])print("\nAttention per-head output-projection norms:")for l in [3,7,11,15,19,23]:    W=load_original(f"model.language_model.layers.{l}.self_attn.o_proj.weight").reshape(1024,8,256)    print(l,np.round(W.square().sum((0,2)).sqrt().numpy(),3).tolist())

# Hypothesis: exact phrase copying is the common failure mechanism; induction-style full-attention heads are a plausible causal bottleneck.def ids_of(s):    t = tok.encode(s)    return list(t.ids) if hasattr(t, "ids") else list(t)def repeated_fraction(ids, n=12):    if len(ids)<n: return 0.    grams=[tuple(ids[i:i+n]) for i in range(len(ids)-n+1)]    return 1-len(set(grams))/len(grams)reps=[]for r in flat1:    ids=ids_of(r["text"])    reps.append([repeated_fraction(ids[:2048]),repeated_fraction(ids[2048:8192]),repeated_fraction(ids[-4096:])])reps=np.array(reps)print("Repeated 12-token fractions: first 2048 / tokens 2048:8192 / last 4096")for label,mask in [("correct",y1),("failed",~y1)]:    print(label,np.round(np.mean(reps[mask],axis=0),3))print("Repeated Wait phrases among failures:",      sum("Wait," in r["text"][-3000:] for r in flat1 if not r["correct"]),"/",int((~y1).sum()))down_cache={}print("\nMLP neurons with largest average residual contribution norms:")for ai,l in enumerate(idx1["mlp_layers"]):    W=load_original(f"model.language_model.layers.{l}.mlp.down_proj.weight")    down_cache[l]=W    norms=W.norm(dim=0).numpy()    score=np.abs(mean_bad[ai]-mean_good[ai])*norms    js=np.argsort(score)[-2:][::-1]    if l>=8:        print(l,[(int(j),round(float(score[j]),3),round(float(mean_bad[ai,j]),3),                  round(float(mean_good[ai,j]),3)) for j in js])print("\nAttention per-head output-projection norms:")for l in [3,7,11,15,19,23]:    W=load_original(f"model.language_model.layers.{l}.self_attn.o_proj.weight").reshape(1024,8,256)    print(l,np.round(W.square().sum((0,2)).sqrt().numpy(),3).tolist())

OUTPUT of Python run 6 of at most 300:

Repeated 12-token fractions: first 2048 / tokens 2048:8192 / last 4096correct [0.039 0.098 0.177]failed [0.07  0.506 0.975]Repeated Wait phrases among failures: 213 / 232
MLP neurons with largest average residual contribution norms:8 [(38, 0.02, -0.073, -0.009), (41, 0.014, 0.088, 0.023)]9 [(18, 0.03, 0.121, 0.017), (42, 0.028, 0.229, 0.04)]10 [(59, 0.039, -0.165, -0.008), (3, 0.036, -0.189, -0.031)]11 [(11, 0.044, 0.183, -0.02), (81, 0.033, 0.165, 0.079)]12 [(1226, 0.05, 0.214, 0.016), (161, 0.024, -0.103, -0.019)]13 [(16, 0.055, 0.195, -0.039), (7, 0.054, -0.274, -0.023)]14 [(239, 0.051, -0.123, 0.086), (334, 0.047, 0.195, 0.01)]15 [(0, 0.091, 0.467, 0.111), (46, 0.052, -0.208, -0.023)]16 [(98, 0.041, -0.314, -0.115), (43, 0.039, 0.246, 0.093)]17 [(20, 0.086, 0.392, 0.007), (119, 0.052, -0.257, -0.05)]18 [(8, 0.207, -0.679, 0.025), (29, 0.078, 0.42, 0.132)]19 [(11, 0.264, -1.193, -0.238), (171, 0.073, -0.327, -0.106)]20 [(89, 0.059, 0.242, 0.064), (77, 0.05, 0.66, 0.461)]21 [(30, 0.102, -0.424, -0.042), (99, 0.062, 0.384, 0.138)]22 [(59, 0.171, 0.823, 0.232), (11, 0.119, -5.902, -5.18)]23 [(22, 0.322, 1.506, 0.161), (46, 0.302, 3.242, 2.279)]
Attention per-head output-projection norms:3 [5.870999813079834, 6.525000095367432, 7.322999954223633, 6.546999931335449, 5.869999885559082, 5.791999816894531, 6.257999897003174, 5.485000133514404]7 [5.570000171661377, 5.583000183105469, 6.021999835968018, 6.695000171661377, 5.632999897003174, 5.369999885559082, 5.4019999504089355, 5.354000091552734]11 [7.033999919891357, 5.136000156402588, 5.394999980926514, 5.677999973297119, 6.178999900817871, 5.425000190734863, 6.9079999923706055, 5.639999866485596]15 [7.0229997634887695, 6.408999919891357, 6.906000137329102, 8.199999809265137, 7.376999855041504, 7.3460001945495605, 6.764999866485596, 6.478000164031982]19 [8.383000373840332, 10.493000030517578, 8.13700008392334, 8.883000373840332, 9.380000114440918, 7.326000213623047, 7.672999858856201, 7.755000114440918]23 [8.14799976348877, 7.500999927520752, 5.288000106811523, 7.0, 7.870999813079834, 7.140999794006348, 5.260000228881836, 7.895999908447266]

Repeated 12-token fractions: first 2048 / tokens 2048:8192 / last 4096correct [0.039 0.098 0.177]failed [0.07  0.506 0.975]Repeated Wait phrases among failures: 213 / 232
MLP neurons with largest average residual contribution norms:8 [(38, 0.02, -0.073, -0.009), (41, 0.014, 0.088, 0.023)]9 [(18, 0.03, 0.121, 0.017), (42, 0.028, 0.229, 0.04)]10 [(59, 0.039, -0.165, -0.008), (3, 0.036, -0.189, -0.031)]11 [(11, 0.044, 0.183, -0.02), (81, 0.033, 0.165, 0.079)]12 [(1226, 0.05, 0.214, 0.016), (161, 0.024, -0.103, -0.019)]13 [(16, 0.055, 0.195, -0.039), (7, 0.054, -0.274, -0.023)]14 [(239, 0.051, -0.123, 0.086), (334, 0.047, 0.195, 0.01)]15 [(0, 0.091, 0.467, 0.111), (46, 0.052, -0.208, -0.023)]16 [(98, 0.041, -0.314, -0.115), (43, 0.039, 0.246, 0.093)]17 [(20, 0.086, 0.392, 0.007), (119, 0.052, -0.257, -0.05)]18 [(8, 0.207, -0.679, 0.025), (29, 0.078, 0.42, 0.132)]19 [(11, 0.264, -1.193, -0.238), (171, 0.073, -0.327, -0.106)]20 [(89, 0.059, 0.242, 0.064), (77, 0.05, 0.66, 0.461)]21 [(30, 0.102, -0.424, -0.042), (99, 0.062, 0.384, 0.138)]22 [(59, 0.171, 0.823, 0.232), (11, 0.119, -5.902, -5.18)]23 [(22, 0.322, 1.506, 0.161), (46, 0.302, 3.242, 2.279)]
Attention per-head output-projection norms:3 [5.870999813079834, 6.525000095367432, 7.322999954223633, 6.546999931335449, 5.869999885559082, 5.791999816894531, 6.257999897003174, 5.485000133514404]7 [5.570000171661377, 5.583000183105469, 6.021999835968018, 6.695000171661377, 5.632999897003174, 5.369999885559082, 5.4019999504089355, 5.354000091552734]11 [7.033999919891357, 5.136000156402588, 5.394999980926514, 5.677999973297119, 6.178999900817871, 5.425000190734863, 6.9079999923706055, 5.639999866485596]15 [7.0229997634887695, 6.408999919891357, 6.906000137329102, 8.199999809265137, 7.376999855041504, 7.3460001945495605, 6.764999866485596, 6.478000164031982]19 [8.383000373840332, 10.493000030517578, 8.13700008392334, 8.883000373840332, 9.380000114440918, 7.326000213623047, 7.672999858856201, 7.755000114440918]23 [8.14799976348877, 7.500999927520752, 5.288000106811523, 7.0, 7.870999813079834, 7.140999794006348, 5.260000228881836, 7.895999908447266]

forward

{"type":"teacher_forced","model":"original","enable_thinking":true,"chats":[[{"role":"user","content":"James has 5 dozen boxes of matches. Each box contains 20 matches. How many matches does James have in total?\nPlease reason step by step, and put your final answer within \\boxed{}."},{"role":"assistant","content":"<think>\nHere's my thought process for solving this problem:\n\n1. **Analyze the Request:**\n   * Given: James has 5 dozen boxes of matches. Each box contains 20 matches.\n   * Goal: Calculate the total number of matches.\n\n2. **Convert dozens to boxes:**\n   * One dozen is 12.\n   * Five dozen is 5 times 12 = 60 boxes.\n\n3. **Calculate the total:**\n   * Each box contains 20 matches.\n   * Total matches = 60 times 20 = 1200.\n\n4. **Check the calculation:**\n   * Alternatively, one dozen boxes contains 12 times 20 = 240 matches.\n   * Five dozen boxes contains 5 times 240 = 1200 matches.\n   * Both calculations agree.\n\n   * Wait, 5 dozen means 60 boxes. Each box contains 20 matches. Therefore, 60 times 20 is 1200 matches.\n   * Wait, 5 dozen means 60 boxes. Each box contains 20 matches. Therefore, 60 times 20 is 1200 matches.\n   * Wait, 5 dozen means 60 boxes. Each box contains 20 matches. Therefore, 60 times 20 is 1200 matches.\n   * Wait, 5 dozen means 60 boxes. Each box contains 20 matches. Therefore, 60 times 20 is 1200 matches.\n   * Wait, 5 dozen means 60 boxes. Each box contains 20 matches. Therefore, 60 times 20 is 1200 matches.\n   * Wait, 5 dozen means 60 boxes. Each box contains 20 matches. Therefore, 60 times 20 is 1200 matches.\n   * Wait, 5 dozen means 60 boxes. Each box contains 20 matches. Therefore, 60 times 20 is 1200 matches.\n   * Wait, 5 dozen means 60 boxes. Each box contains 20 matches. Therefore, 60 times 20 is 1200 matches.\n\n   I will write the response.\n   I will write the response.\n   I will write the response.\n   I will write the response.\n   I will write the response.\n   I will write the response.\n   I will write the response.\n   I will write the response.\n\nA different check is to group the 60 boxes into 6 groups of 10. Each group has 200 matches, so 6 groups have 1200 matches. The units are matches rather than boxes. No boxes have been removed."}],[{"role":"user","content":"Compute $\\tan(225^\\circ)$.\nPlease reason step by step, and put your final answer within \\boxed{}."},{"role":"assistant","content":"<think>\nHere's a thinking process that leads to the solution:\n\n1. **Analyze the Request:**\n   * Task: Compute tan(225 degrees).\n   * Format: Step-by-step reasoning, ending with a boxed answer.\n\n2. **Use the period of tangent:**\n   * Tangent has period 180 degrees.\n   * 225 degrees = 180 degrees + 45 degrees.\n   * Therefore tan(225 degrees) = tan(45 degrees) = 1.\n\n3. **Check the sign:**\n   * 225 degrees is in the third quadrant.\n   * Sine and cosine are both negative there.\n   * The ratio of sine to cosine is positive.\n   * sin(225 degrees) = -sqrt(2)/2 and cos(225 degrees) = -sqrt(2)/2.\n   * Dividing gives 1.\n\n   * Wait, tan(225 degrees) = 1.\n   * Wait, let me check the calculator again.\n   * Wait, tan(225 degrees) = 1.\n   * Wait, let me check the calculator again.\n   * Wait, tan(225 degrees) = 1.\n   * Wait, let me check the calculator again.\n   * Wait, tan(225 degrees) = 1.\n   * Wait, let me check the calculator again.\n   * Wait, tan(225 degrees) = 1.\n   * Wait, let me check the calculator again.\n   * Wait, tan(225 degrees) = 1.\n   * Wait, let me check the calculator again.\n   * Wait, tan(225 degrees) = 1.\n   * Wait, let me check the calculator again.\n   * Wait, tan(225 degrees) = 1.\n   * Wait, let me check the calculator again.\n\n   * Wait, the angle is in the third quadrant. Both sine and cosine are negative. Their ratio is positive.\n   * Wait, the angle is in the third quadrant. Both sine and cosine are negative. Their ratio is positive.\n   * Wait, the angle is in the third quadrant. Both sine and cosine are negative. Their ratio is positive.\n   * Wait, the angle is in the third quadrant. Both sine and cosine are negative. Their ratio is positive.\n\nThe unit circle provides another verification: the point at 225 degrees has equal negative coordinates, and its slope from the origin is 1. This agrees with tangent's period of 180 degrees."}]],"return":{"token_logprobs":true,"topk":10,"hidden_states":[0,4,8,12,16,20,24],"mlp_act":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23],"attn_layers":[3,7,11,15,19,23]}}

{"type":"teacher_forced","model":"original","enable_thinking":true,"chats":[[{"role":"user","content":"James has 5 dozen boxes of matches. Each box contains 20 matches. How many matches does James have in total?\nPlease reason step by step, and put your final answer within \\boxed{}."},{"role":"assistant","content":"<think>\nHere's my thought process for solving this problem:\n\n1. **Analyze the Request:**\n   * Given: James has 5 dozen boxes of matches. Each box contains 20 matches.\n   * Goal: Calculate the total number of matches.\n\n2. **Convert dozens to boxes:**\n   * One dozen is 12.\n   * Five dozen is 5 times 12 = 60 boxes.\n\n3. **Calculate the total:**\n   * Each box contains 20 matches.\n   * Total matches = 60 times 20 = 1200.\n\n4. **Check the calculation:**\n   * Alternatively, one dozen boxes contains 12 times 20 = 240 matches.\n   * Five dozen boxes contains 5 times 240 = 1200 matches.\n   * Both calculations agree.\n\n   * Wait, 5 dozen means 60 boxes. Each box contains 20 matches. Therefore, 60 times 20 is 1200 matches.\n   * Wait, 5 dozen means 60 boxes. Each box contains 20 matches. Therefore, 60 times 20 is 1200 matches.\n   * Wait, 5 dozen means 60 boxes. Each box contains 20 matches. Therefore, 60 times 20 is 1200 matches.\n   * Wait, 5 dozen means 60 boxes. Each box contains 20 matches. Therefore, 60 times 20 is 1200 matches.\n   * Wait, 5 dozen means 60 boxes. Each box contains 20 matches. Therefore, 60 times 20 is 1200 matches.\n   * Wait, 5 dozen means 60 boxes. Each box contains 20 matches. Therefore, 60 times 20 is 1200 matches.\n   * Wait, 5 dozen means 60 boxes. Each box contains 20 matches. Therefore, 60 times 20 is 1200 matches.\n   * Wait, 5 dozen means 60 boxes. Each box contains 20 matches. Therefore, 60 times 20 is 1200 matches.\n\n   I will write the response.\n   I will write the response.\n   I will write the response.\n   I will write the response.\n   I will write the response.\n   I will write the response.\n   I will write the response.\n   I will write the response.\n\nA different check is to group the 60 boxes into 6 groups of 10. Each group has 200 matches, so 6 groups have 1200 matches. The units are matches rather than boxes. No boxes have been removed."}],[{"role":"user","content":"Compute $\\tan(225^\\circ)$.\nPlease reason step by step, and put your final answer within \\boxed{}."},{"role":"assistant","content":"<think>\nHere's a thinking process that leads to the solution:\n\n1. **Analyze the Request:**\n   * Task: Compute tan(225 degrees).\n   * Format: Step-by-step reasoning, ending with a boxed answer.\n\n2. **Use the period of tangent:**\n   * Tangent has period 180 degrees.\n   * 225 degrees = 180 degrees + 45 degrees.\n   * Therefore tan(225 degrees) = tan(45 degrees) = 1.\n\n3. **Check the sign:**\n   * 225 degrees is in the third quadrant.\n   * Sine and cosine are both negative there.\n   * The ratio of sine to cosine is positive.\n   * sin(225 degrees) = -sqrt(2)/2 and cos(225 degrees) = -sqrt(2)/2.\n   * Dividing gives 1.\n\n   * Wait, tan(225 degrees) = 1.\n   * Wait, let me check the calculator again.\n   * Wait, tan(225 degrees) = 1.\n   * Wait, let me check the calculator again.\n   * Wait, tan(225 degrees) = 1.\n   * Wait, let me check the calculator again.\n   * Wait, tan(225 degrees) = 1.\n   * Wait, let me check the calculator again.\n   * Wait, tan(225 degrees) = 1.\n   * Wait, let me check the calculator again.\n   * Wait, tan(225 degrees) = 1.\n   * Wait, let me check the calculator again.\n   * Wait, tan(225 degrees) = 1.\n   * Wait, let me check the calculator again.\n   * Wait, tan(225 degrees) = 1.\n   * Wait, let me check the calculator again.\n\n   * Wait, the angle is in the third quadrant. Both sine and cosine are negative. Their ratio is positive.\n   * Wait, the angle is in the third quadrant. Both sine and cosine are negative. Their ratio is positive.\n   * Wait, the angle is in the third quadrant. Both sine and cosine are negative. Their ratio is positive.\n   * Wait, the angle is in the third quadrant. Both sine and cosine are negative. Their ratio is positive.\n\nThe unit circle provides another verification: the point at 225 degrees has equal negative coordinates, and its slope from the origin is 1. This agrees with tangent's period of 180 degrees."}]],"return":{"token_logprobs":true,"topk":10,"hidden_states":[0,4,8,12,16,20,24],"mlp_act":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23],"attn_layers":[3,7,11,15,19,23]}}

The forward pass failed and was not counted: IndexError: tuple index out of range

python

# Hypothesis: the failed diagnostic request was an instrumentation limitation; inspect partial outputs before reducing requested attention layers.print("Work entries:", sorted(os.listdir("/work")))for p in sorted(Path("/work").glob("fwd*")):    print(p.name, [(x.name, x.stat().st_size) for x in p.iterdir()][:40])print("Activation magnitudes by neuron-index block (late layers):")for l in [11,18,19,22,23]:    a=idx1["mlp_layers"].index(l)    print(l,[(s,round(float(np.abs(M1[:,a,s:s+64]).mean()),4)) for s in [0,64,128,512,1024,2048,3072]])

# Hypothesis: the failed diagnostic request was an instrumentation limitation; inspect partial outputs before reducing requested attention layers.print("Work entries:", sorted(os.listdir("/work")))for p in sorted(Path("/work").glob("fwd*")):    print(p.name, [(x.name, x.stat().st_size) for x in p.iterdir()][:40])print("Activation magnitudes by neuron-index block (late layers):")for l in [11,18,19,22,23]:    a=idx1["mlp_layers"].index(l)    print(l,[(s,round(float(np.abs(M1[:,a,s:s+64]).mean()),4)) for s in [0,64,128,512,1024,2048,3072]])

OUTPUT of Python run 7 of at most 300:

Work entries: ['gen_1.jsonl', 'gen_1_acts']Activation magnitudes by neuron-index block (late layers):11 [(0, 0.0348), (64, 0.0294), (128, 0.0271), (512, 0.0142), (1024, 0.0122), (2048, 0.0093), (3072, 0.009)]18 [(0, 0.1812), (64, 0.097), (128, 0.0624), (512, 0.0357), (1024, 0.0275), (2048, 0.0223), (3072, 0.0231)]19 [(0, 0.2297), (64, 0.1014), (128, 0.0943), (512, 0.0301), (1024, 0.0239), (2048, 0.0227), (3072, 0.0255)]22 [(0, 0.3335), (64, 0.1409), (128, 0.1158), (512, 0.069), (1024, 0.045), (2048, 0.032), (3072, 0.0374)]23 [(0, 0.5288), (64, 0.3264), (128, 0.293), (512, 0.1312), (1024, 0.0815), (2048, 0.0354), (3072, 0.0242)]

Work entries: ['gen_1.jsonl', 'gen_1_acts']Activation magnitudes by neuron-index block (late layers):11 [(0, 0.0348), (64, 0.0294), (128, 0.0271), (512, 0.0142), (1024, 0.0122), (2048, 0.0093), (3072, 0.009)]18 [(0, 0.1812), (64, 0.097), (128, 0.0624), (512, 0.0357), (1024, 0.0275), (2048, 0.0223), (3072, 0.0231)]19 [(0, 0.2297), (64, 0.1014), (128, 0.0943), (512, 0.0301), (1024, 0.0239), (2048, 0.0227), (3072, 0.0255)]22 [(0, 0.3335), (64, 0.1409), (128, 0.1158), (512, 0.069), (1024, 0.045), (2048, 0.032), (3072, 0.0374)]23 [(0, 0.5288), (64, 0.3264), (128, 0.293), (512, 0.1312), (1024, 0.0815), (2048, 0.0354), (3072, 0.0242)]

forward

{"type":"teacher_forced","model":"original","enable_thinking":true,"chats":[[{"role":"user","content":"James has 5 dozen boxes of matches. Each box contains 20 matches. How many matches does James have in total?\nPlease reason step by step, and put your final answer within \\boxed{}."},{"role":"assistant","content":"<think>\nHere's my thought process for solving this problem:\n\n1. **Analyze the Request:**\n   * Given: James has 5 dozen boxes of matches. Each box contains 20 matches.\n   * Goal: Calculate the total number of matches.\n\n2. **Convert dozens to boxes:**\n   * One dozen is 12.\n   * Five dozen is 5 times 12 = 60 boxes.\n\n3. **Calculate the total:**\n   * Each box contains 20 matches.\n   * Total matches = 60 times 20 = 1200.\n\n4. **Check the calculation:**\n   * Alternatively, one dozen boxes contains 12 times 20 = 240 matches.\n   * Five dozen boxes contains 5 times 240 = 1200 matches.\n   * Both calculations agree.\n\n   * Wait, 5 dozen means 60 boxes. Each box contains 20 matches. Therefore, 60 times 20 is 1200 matches.\n   * Wait, 5 dozen means 60 boxes. Each box contains 20 matches. Therefore, 60 times 20 is 1200 matches.\n   * Wait, 5 dozen means 60 boxes. Each box contains 20 matches. Therefore, 60 times 20 is 1200 matches.\n   * Wait, 5 dozen means 60 boxes. Each box contains 20 matches. Therefore, 60 times 20 is 1200 matches.\n   * Wait, 5 dozen means 60 boxes. Each box contains 20 matches. Therefore, 60 times 20 is 1200 matches.\n   * Wait, 5 dozen means 60 boxes. Each box contains 20 matches. Therefore, 60 times 20 is 1200 matches.\n   * Wait, 5 dozen means 60 boxes. Each box contains 20 matches. Therefore, 60 times 20 is 1200 matches.\n   * Wait, 5 dozen means 60 boxes. Each box contains 20 matches. Therefore, 60 times 20 is 1200 matches.\n\n   I will write the response.\n   I will write the response.\n   I will write the response.\n   I will write the response.\n   I will write the response.\n   I will write the response.\n   I will write the response.\n   I will write the response.\n\nA different check is to group the 60 boxes into 6 groups of 10. Each group has 200 matches, so 6 groups have 1200 matches. The units are matches rather than boxes. No boxes have been removed."}],[{"role":"user","content":"Compute $\\tan(225^\\circ)$.\nPlease reason step by step, and put your final answer within \\boxed{}."},{"role":"assistant","content":"<think>\nHere's a thinking process that leads to the solution:\n\n1. **Analyze the Request:**\n   * Task: Compute tan(225 degrees).\n   * Format: Step-by-step reasoning, ending with a boxed answer.\n\n2. **Use the period of tangent:**\n   * Tangent has period 180 degrees.\n   * 225 degrees = 180 degrees + 45 degrees.\n   * Therefore tan(225 degrees) = tan(45 degrees) = 1.\n\n3. **Check the sign:**\n   * 225 degrees is in the third quadrant.\n   * Sine and cosine are both negative there.\n   * The ratio of sine to cosine is positive.\n   * sin(225 degrees) = -sqrt(2)/2 and cos(225 degrees) = -sqrt(2)/2.\n   * Dividing gives 1.\n\n   * Wait, tan(225 degrees) = 1.\n   * Wait, let me check the calculator again.\n   * Wait, tan(225 degrees) = 1.\n   * Wait, let me check the calculator again.\n   * Wait, tan(225 degrees) = 1.\n   * Wait, let me check the calculator again.\n   * Wait, tan(225 degrees) = 1.\n   * Wait, let me check the calculator again.\n   * Wait, tan(225 degrees) = 1.\n   * Wait, let me check the calculator again.\n   * Wait, tan(225 degrees) = 1.\n   * Wait, let me check the calculator again.\n   * Wait, tan(225 degrees) = 1.\n   * Wait, let me check the calculator again.\n   * Wait, tan(225 degrees) = 1.\n   * Wait, let me check the calculator again.\n\n   * Wait, the angle is in the third quadrant. Both sine and cosine are negative. Their ratio is positive.\n   * Wait, the angle is in the third quadrant. Both sine and cosine are negative. Their ratio is positive.\n   * Wait, the angle is in the third quadrant. Both sine and cosine are negative. Their ratio is positive.\n   * Wait, the angle is in the third quadrant. Both sine and cosine are negative. Their ratio is positive.\n\nThe unit circle provides another verification: the point at 225 degrees has equal negative coordinates, and its slope from the origin is 1. This agrees with tangent's period of 180 degrees."}]],"return":{"token_logprobs":true,"topk":10,"hidden_states":[0,4,8,12,16,20,24],"mlp_act":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23]}}

{"type":"teacher_forced","model":"original","enable_thinking":true,"chats":[[{"role":"user","content":"James has 5 dozen boxes of matches. Each box contains 20 matches. How many matches does James have in total?\nPlease reason step by step, and put your final answer within \\boxed{}."},{"role":"assistant","content":"<think>\nHere's my thought process for solving this problem:\n\n1. **Analyze the Request:**\n   * Given: James has 5 dozen boxes of matches. Each box contains 20 matches.\n   * Goal: Calculate the total number of matches.\n\n2. **Convert dozens to boxes:**\n   * One dozen is 12.\n   * Five dozen is 5 times 12 = 60 boxes.\n\n3. **Calculate the total:**\n   * Each box contains 20 matches.\n   * Total matches = 60 times 20 = 1200.\n\n4. **Check the calculation:**\n   * Alternatively, one dozen boxes contains 12 times 20 = 240 matches.\n   * Five dozen boxes contains 5 times 240 = 1200 matches.\n   * Both calculations agree.\n\n   * Wait, 5 dozen means 60 boxes. Each box contains 20 matches. Therefore, 60 times 20 is 1200 matches.\n   * Wait, 5 dozen means 60 boxes. Each box contains 20 matches. Therefore, 60 times 20 is 1200 matches.\n   * Wait, 5 dozen means 60 boxes. Each box contains 20 matches. Therefore, 60 times 20 is 1200 matches.\n   * Wait, 5 dozen means 60 boxes. Each box contains 20 matches. Therefore, 60 times 20 is 1200 matches.\n   * Wait, 5 dozen means 60 boxes. Each box contains 20 matches. Therefore, 60 times 20 is 1200 matches.\n   * Wait, 5 dozen means 60 boxes. Each box contains 20 matches. Therefore, 60 times 20 is 1200 matches.\n   * Wait, 5 dozen means 60 boxes. Each box contains 20 matches. Therefore, 60 times 20 is 1200 matches.\n   * Wait, 5 dozen means 60 boxes. Each box contains 20 matches. Therefore, 60 times 20 is 1200 matches.\n\n   I will write the response.\n   I will write the response.\n   I will write the response.\n   I will write the response.\n   I will write the response.\n   I will write the response.\n   I will write the response.\n   I will write the response.\n\nA different check is to group the 60 boxes into 6 groups of 10. Each group has 200 matches, so 6 groups have 1200 matches. The units are matches rather than boxes. No boxes have been removed."}],[{"role":"user","content":"Compute $\\tan(225^\\circ)$.\nPlease reason step by step, and put your final answer within \\boxed{}."},{"role":"assistant","content":"<think>\nHere's a thinking process that leads to the solution:\n\n1. **Analyze the Request:**\n   * Task: Compute tan(225 degrees).\n   * Format: Step-by-step reasoning, ending with a boxed answer.\n\n2. **Use the period of tangent:**\n   * Tangent has period 180 degrees.\n   * 225 degrees = 180 degrees + 45 degrees.\n   * Therefore tan(225 degrees) = tan(45 degrees) = 1.\n\n3. **Check the sign:**\n   * 225 degrees is in the third quadrant.\n   * Sine and cosine are both negative there.\n   * The ratio of sine to cosine is positive.\n   * sin(225 degrees) = -sqrt(2)/2 and cos(225 degrees) = -sqrt(2)/2.\n   * Dividing gives 1.\n\n   * Wait, tan(225 degrees) = 1.\n   * Wait, let me check the calculator again.\n   * Wait, tan(225 degrees) = 1.\n   * Wait, let me check the calculator again.\n   * Wait, tan(225 degrees) = 1.\n   * Wait, let me check the calculator again.\n   * Wait, tan(225 degrees) = 1.\n   * Wait, let me check the calculator again.\n   * Wait, tan(225 degrees) = 1.\n   * Wait, let me check the calculator again.\n   * Wait, tan(225 degrees) = 1.\n   * Wait, let me check the calculator again.\n   * Wait, tan(225 degrees) = 1.\n   * Wait, let me check the calculator again.\n   * Wait, tan(225 degrees) = 1.\n   * Wait, let me check the calculator again.\n\n   * Wait, the angle is in the third quadrant. Both sine and cosine are negative. Their ratio is positive.\n   * Wait, the angle is in the third quadrant. Both sine and cosine are negative. Their ratio is positive.\n   * Wait, the angle is in the third quadrant. Both sine and cosine are negative. Their ratio is positive.\n   * Wait, the angle is in the third quadrant. Both sine and cosine are negative. Their ratio is positive.\n\nThe unit circle provides another verification: the point at 225 degrees has equal negative coordinates, and its slope from the origin is 1. This agrees with tangent's period of 180 degrees."}]],"return":{"token_logprobs":true,"topk":10,"hidden_states":[0,4,8,12,16,20,24],"mlp_act":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23]}}

Forward pass 2 of 10 done (teacher_forced); results in /work/fwd_3/. Summary:

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{ "ok": true, "lengths": [  679,  613 ], "files": {  "input_ids": [   2,   679  ],  "token_logprobs": [   2,   678  ],  "topk_logprobs": [   2,   679,   10  ],  "topk_ids": [   2,   679,   10  ],  "hidden_0": [   2,   679,   1024  ],  "hidden_4": [   2,   679,   1024  ],  "hidden_8": [   2,   679,   1024  ],  "hidden_12": [   2,   679,   1024  ],  "hidden_16": [   2,   679,   1024  ],  "hidden_20": [   2,   679,   1024  ],  "hidden_24": [   2,   679,   1024  ],  "mlp_act_0": [   2,   679,   3584  ],  "mlp_act_1": [   2,   679,   3584  ],  "mlp_act_2": [   2,   679,   3584  ],  "mlp_act_3": [   2,   679,   3584  ],  "mlp_act_4": [   2,   679,   3584  ],  "mlp_act_5": [   2,   679,   3584  ],  "mlp_act_6": [   2,   679,   3584  ],  "mlp_act_7": [   2,   679,   3584  ],  "mlp_act_8": [   2,   679,   3584  ],  "mlp_act_9": [   2,   679,   3584  ],  "mlp_act_10": [   2,   679,   3584  ],  "mlp_act_11": [   2,   679,   3584  ],  "mlp_act_12": [   2,   679,   3584  ],  "mlp_act_13": [   2,   679,   3584  ],  "mlp_act_14": [   2,   679,   3584  ],  "mlp_act_15": [   2,   679,   3584  ],  "mlp_act_16": [   2,   679,   3584  ],  "mlp_act_17": [   2,   679,   3584  ],  "mlp_act_18": [   2,   679,   3584  ],  "mlp_act_19": [   2,   679,   3584  ],  "mlp_act_20": [   2,   679,   3584  ],  "mlp_act_21": [   2,   679,   3584  ],  "mlp_act_22": [   2,   679,   3584  ],  "mlp_act_23": [   2,   679,   3584  ] }, "edited_tensors": 0, "mean_token_logprob": [  -0.8206353187561035,  -0.8587836027145386 ], "type": "teacher_forced", "s": 1.2}

Forward passes left: 8.

python

# Hypothesis: neurons whose activity grows across identical repetitions, and also tracks real failed generations, are candidates for a repetition-feedback circuit.fwdroot=Path("/work/fwd_3")meta3=json.load(open(fwdroot/"meta.json"))print("Metadata:",str(meta3)[:1500])I3=np.load(fwdroot/"input_ids.npy")LP3=np.load(fwdroot/"token_logprobs.npy")def subseq_spans(seq,needle):    return [(i,i+len(needle)) for i in range(len(seq)-len(needle)+1)            if np.array_equal(seq[i:i+len(needle)],needle)]phrases=[" Wait, 5 dozen means 60 boxes. Each box contains 20 matches. Therefore, 60 times 20 is 1200 matches."," Wait, tan(225 degrees) = 1."," Wait, let me check the calculator again."," I will write the response."," Wait, the angle is in the third quadrant. Both sine and cosine are negative. Their ratio is positive."]spans3=[]for p in phrases:    n=ids_of(p)    spans=[subseq_spans(I3[i],n) for i in range(2)]    spans3.append(spans)    print(p[:70], "length",len(n),"spans",spans)TFM={l:np.load(fwdroot/f"mlp_act_{l}.npy") for l in range(24)}repeat_deltas={}dense_rows=[]for l in range(1,24):    a=idx1["mlp_layers"].index(l)    D=[]; C=[]; R=[]    for pi in [0,1,2,3,4]:        bi=0 if pi in [0,3] else 1        spans=spans3[pi][bi]        if len(spans)<3: continue        blocks=np.stack([TFM[l][bi,s:e].mean(0) for s,e in spans])        D.append(blocks[-3:].mean(0)-blocks[0])        C.append(blocks[0]); R.append(blocks[-3:].mean(0))    D=np.stack(D); C=np.stack(C); R=np.stack(R)    repeat_deltas[l]=D    norms=down_cache[l].norm(dim=0).numpy()    generation_delta=mean_bad[a]-mean_good[a]    # Agreement in direction across independent repetitive contents, weighted by actual residual contribution.    agree=np.mean(D*generation_delta>0,axis=0)    score=np.sqrt(np.abs(D.mean(0)*generation_delta))*norms*agree    for j in np.argsort(score)[-6:]:        dense_rows.append((float(score[j]),l,int(j),float(generation_delta[j]),                           float(D[:,j].mean()),float(agree[j]),np.round(D[:,j],3).tolist()))print("Cross-context repetition-associated neurons (score, layer, neuron, generation delta, within-text delta, agreement, individual deltas):")for row in sorted(dense_rows,reverse=True)[:30]:    print(row)

# Hypothesis: neurons whose activity grows across identical repetitions, and also tracks real failed generations, are candidates for a repetition-feedback circuit.fwdroot=Path("/work/fwd_3")meta3=json.load(open(fwdroot/"meta.json"))print("Metadata:",str(meta3)[:1500])I3=np.load(fwdroot/"input_ids.npy")LP3=np.load(fwdroot/"token_logprobs.npy")def subseq_spans(seq,needle):    return [(i,i+len(needle)) for i in range(len(seq)-len(needle)+1)            if np.array_equal(seq[i:i+len(needle)],needle)]phrases=[" Wait, 5 dozen means 60 boxes. Each box contains 20 matches. Therefore, 60 times 20 is 1200 matches."," Wait, tan(225 degrees) = 1."," Wait, let me check the calculator again."," I will write the response."," Wait, the angle is in the third quadrant. Both sine and cosine are negative. Their ratio is positive."]spans3=[]for p in phrases:    n=ids_of(p)    spans=[subseq_spans(I3[i],n) for i in range(2)]    spans3.append(spans)    print(p[:70], "length",len(n),"spans",spans)TFM={l:np.load(fwdroot/f"mlp_act_{l}.npy") for l in range(24)}repeat_deltas={}dense_rows=[]for l in range(1,24):    a=idx1["mlp_layers"].index(l)    D=[]; C=[]; R=[]    for pi in [0,1,2,3,4]:        bi=0 if pi in [0,3] else 1        spans=spans3[pi][bi]        if len(spans)<3: continue        blocks=np.stack([TFM[l][bi,s:e].mean(0) for s,e in spans])        D.append(blocks[-3:].mean(0)-blocks[0])        C.append(blocks[0]); R.append(blocks[-3:].mean(0))    D=np.stack(D); C=np.stack(C); R=np.stack(R)    repeat_deltas[l]=D    norms=down_cache[l].norm(dim=0).numpy()    generation_delta=mean_bad[a]-mean_good[a]    # Agreement in direction across independent repetitive contents, weighted by actual residual contribution.    agree=np.mean(D*generation_delta>0,axis=0)    score=np.sqrt(np.abs(D.mean(0)*generation_delta))*norms*agree    for j in np.argsort(score)[-6:]:        dense_rows.append((float(score[j]),l,int(j),float(generation_delta[j]),                           float(D[:,j].mean()),float(agree[j]),np.round(D[:,j],3).tolist()))print("Cross-context repetition-associated neurons (score, layer, neuron, generation delta, within-text delta, agreement, individual deltas):")for row in sorted(dense_rows,reverse=True)[:30]:    print(row)

[note: the harness was restarted, so your kernel restarted too and its variables are gone] OUTPUT of Python run 8 of at most 300:

Metadata: {'texts': ["<|im_start|>user\nJames has 5 dozen boxes of matches. Each box contains 20 matches. How many matches does James have in total?\nPlease reason step by step, and put your final answer within \\boxed{}.<|im_end|>\n<|im_start|>assistant\n<think>\n\n</think>\n\n<think>\nHere's my thought process for solving this problem:\n\n1. **Analyze the Request:**\n   * Given: James has 5 dozen boxes of matches. Each box contains 20 matches.\n   * Goal: Calculate the total number of matches.\n\n2. **Convert dozens to boxes:**\n   * One dozen is 12.\n   * Five dozen is 5 times 12 = 60 boxes.\n\n3. **Calculate the total:**\n   * Each box contains 20 matches.\n   * Total matches = 60 times 20 = 1200.\n\n4. **Check the calculation:**\n   * Alternatively, one dozen boxes contains 12 times 20 = 240 matches.\n   * Five dozen boxes contains 5 times 240 = 1200 matches.\n   * Both calculations agree.\n\n   * Wait, 5 dozen means 60 boxes. Each box contains 20 matches. Therefore, 60 times 20 is 1200 matches.\n   * Wait, 5 dozen means 60 boxes. Each box contains 20 matches. Therefore, 60 times 20 is 1200 matches.\n   * Wait, 5 dozen means 60 boxes. Each box contains 20 matches. Therefore, 60 times 20 is 1200 matches.\n   * Wait, 5 dozen means 60 boxes. Each box contains 20 matches. Therefore, 60 times 20 is 1200 matches.\n   * Wait, 5 dozen means 60 boxes. Each box contains 20 matches. Therefore, 60 times 20 is 1200 matches.\n   * Wait, 5 dozen means 60 boxes. Each box contains 20 matches. Ther Wait, 5 dozen means 60 boxes. Each box contains 20 matches. Therefore length 36 spans [[(248, 284), (287, 323), (326, 362), (365, 401), (404, 440), (443, 479), (482, 518), (521, 557)], []] Wait, tan(225 degrees) = 1. length 13 spans [[], [(246, 259), (274, 287), (302, 315), (330, 343), (358, 371), (386, 399), (414, 427), (442, 455)]] Wait, let me check the calculator again. length 9 spans [[], [(262, 271), (290, 299), (318, 327), (346, 355), (374, 383), (402, 411), (430, 439), (458, 467)]] I will write the response. length 6 spans [[(559, 565), (567, 573), (575, 581), (583, 589), (591, 597), (599, 605), (607, 613), (615, 621)], []] Wait, the angle is in the third quadrant. Both sine and cosine are ne length 22 spans [[], [(470, 492), (495, 517), (520, 542), (545, 567)]]Cross-context repetition-associated neurons (score, layer, neuron, generation delta, within-text delta, agreement, individual deltas):(0.5012192130088806, 23, 22, 1.3447265625, 3.259765625, 1.0, [4.2578125, 3.029296875, 3.18359375, 2.2578125, 3.5703125])(0.46885350346565247, 23, 40, -1.13671875, -4.1875, 1.0, [-4.3359375, -3.552734375, -5.1640625, -4.05078125, -3.83984375])(0.4125770032405853, 23, 46, 0.962890625, 1.7958984375, 1.0, [3.1953125, 2.123046875, 1.94140625, 0.8291015625, 0.89013671875])(0.33822453022003174, 19, 11, -0.95556640625, -1.5712890625, 1.0, [-2.416015625, -2.244140625, -1.306640625, -1.009765625, -0.8798828125])(0.3285178244113922, 23, 94, 0.55322265625, 2.177734375, 1.0, [2.0234375, 1.818359375, 2.62890625, 2.591796875, 1.822265625])(0.30333033204078674, 18, 8, -0.70361328125, -1.509765625, 1.0, [-1.763671875, -1.4814453125, -1.2607421875, -1.185546875, -1.85546875])(0.2964679002761841, 23, 19, 0.599609375, 1.96875, 1.0, [1.9931640625, 2.154296875, 2.76953125, 1.90234375, 1.025390625])(0.2360299676656723, 22, 59, 0.5908203125, 1.1298828125, 1.0, [1.708984375, 1.138671875, 1.1376953125, 0.7080078125, 0.955078125])(0.1923191398382187, 22, 11, -0.72265625, -1.8837890625, 1.0, [-1.74609375, -1.45703125, -1.90234375, -2.19140625, -2.12109375])(0.185051828622818, 23, 350, -0.371826171875, -0.97216796875, 1.0, [-1.072265625, -0.916015625, -0.94189453125, -0.86376953125, -1.0673828125])(0.1638759970664978, 22, 89, -0.379150390625, -0.94140625, 1.0, [-1.4140625, -0.8828125, -0.9248046875, -0.56689453125, -0.91796875])(0.1326119601726532, 22, 38, -0.294921875, -0.88037109375, 1.0, [-0.8017578125, -1.27734375, -0.98291015625, -0.64208984375, -0.6982421875])(0.1282532960176468, 18, 29, 0.28759765625, 0.77685546875, 1.0, [1.029296875, 0.576171875, 0.9599609375, 0.55419921875, 0.76611328125])(0.12756240367889404, 22, 20, 0.305419921875, 0.8916015625, 1.0, [0.8310546875, 0.86181640625, 0.85986328125, 1.1171875, 0.7890625])(0.12283069640398026, 22, 7, 0.41748046875, 0.50634765625, 1.0, [0.93603515625, 0.406982421875, 0.39794921875, 0.2020263671875, 0.5888671875])(0.11640322208404541, 19, 1, 0.2607421875, 0.75830078125, 1.0, [0.6162109375, 0.744140625, 1.2236328125, 0.7060546875, 0.501953125])(0.11629579961299896, 15, 0, 0.35546875, 0.5869140625, 1.0, [0.6982421875, 0.75390625, 0.471923828125, 0.56982421875, 0.44189453125])(0.11597020924091339, 21, 30, -0.382080078125, -0.4892578125, 1.0, [-0.9482421875, -0.9150390625, -0.22705078125, -0.08197021484375, -0.27392578125])(0.10994921624660492, 19, 171, -0.220947265625, -0.49462890625, 1.0, [-0.65380859375, -0.5458984375, -0.6220703125, -0.1190185546875, -0.5322265625])(0.10250584036111832, 18, 32, 0.225830078125, 0.59033203125, 1.0, [0.73486328125, 0.77197265625, 0.407958984375, 0.408935546875, 0.6298828125])(0.10077561438083649, 20, 89, 0.178466796875, 0.5263671875, 1.0, [0.69189453125, 0.37890625, 0.5341796875, 0.330078125, 0.69677734375])(0.09288061857223512, 17, 20, 0.38525390625, 0.71044921875, 0.8, [1.537109375, 0.97802734375, 0.20703125, -0.2919921875, 1.1220703125])(0.08757990598678589, 12, 1226, 0.1986083984375, 0.611328125, 1.0, [0.52197265625, 0.60595703125, 0.8798828125, 0.80517578125, 0.2430419921875])(0.08523226529359818, 20, 77, 0.19921875, 0.58837890625, 1.0, [0.60791015625, 0.43505859375, 0.7001953125, 0.658203125, 0.541015625])(0.08261194825172424, 18, 106, 0.2451171875, 0.379638671875, 1.0, [0.6328125, 0.5390625, 0.27099609375, 0.10699462890625, 0.34912109375])(0.07991641014814377, 18, 1, -0.237060546875, -0.615234375, 1.0, [-0.40087890625, -0.47998046875, -0.6201171875, -0.94580078125, -0.6298828125])(0.07956857234239578, 14, 390, 0.172119140625, 0.5654296875, 1.0, [0.4970703125, 0.52685546875, 0.73779296875, 0.669921875, 0.39501953125])(0.07937994599342346, 18, 64, -0.1790771484375, -0.4970703125, 1.0, [-0.56982421875, -0.337890625, -0.5322265625, -0.5068359375, -0.5390625])(0.07718387991189957, 13, 7, -0.2509765625, -0.5087890625, 1.0, [-0.7431640625, -0.5888671875, -0.458984375, -0.23095703125, -0.52197265625])(0.07632462680339813, 14, 239, -0.2091064453125, -0.470947265625, 1.0, [-0.751953125, -0.3740234375, -0.323974609375, -0.18994140625, -0.7138671875])

Metadata: {'texts': ["<|im_start|>user\nJames has 5 dozen boxes of matches. Each box contains 20 matches. How many matches does James have in total?\nPlease reason step by step, and put your final answer within \\boxed{}.<|im_end|>\n<|im_start|>assistant\n<think>\n\n</think>\n\n<think>\nHere's my thought process for solving this problem:\n\n1. **Analyze the Request:**\n   * Given: James has 5 dozen boxes of matches. Each box contains 20 matches.\n   * Goal: Calculate the total number of matches.\n\n2. **Convert dozens to boxes:**\n   * One dozen is 12.\n   * Five dozen is 5 times 12 = 60 boxes.\n\n3. **Calculate the total:**\n   * Each box contains 20 matches.\n   * Total matches = 60 times 20 = 1200.\n\n4. **Check the calculation:**\n   * Alternatively, one dozen boxes contains 12 times 20 = 240 matches.\n   * Five dozen boxes contains 5 times 240 = 1200 matches.\n   * Both calculations agree.\n\n   * Wait, 5 dozen means 60 boxes. Each box contains 20 matches. Therefore, 60 times 20 is 1200 matches.\n   * Wait, 5 dozen means 60 boxes. Each box contains 20 matches. Therefore, 60 times 20 is 1200 matches.\n   * Wait, 5 dozen means 60 boxes. Each box contains 20 matches. Therefore, 60 times 20 is 1200 matches.\n   * Wait, 5 dozen means 60 boxes. Each box contains 20 matches. Therefore, 60 times 20 is 1200 matches.\n   * Wait, 5 dozen means 60 boxes. Each box contains 20 matches. Therefore, 60 times 20 is 1200 matches.\n   * Wait, 5 dozen means 60 boxes. Each box contains 20 matches. Ther Wait, 5 dozen means 60 boxes. Each box contains 20 matches. Therefore length 36 spans [[(248, 284), (287, 323), (326, 362), (365, 401), (404, 440), (443, 479), (482, 518), (521, 557)], []] Wait, tan(225 degrees) = 1. length 13 spans [[], [(246, 259), (274, 287), (302, 315), (330, 343), (358, 371), (386, 399), (414, 427), (442, 455)]] Wait, let me check the calculator again. length 9 spans [[], [(262, 271), (290, 299), (318, 327), (346, 355), (374, 383), (402, 411), (430, 439), (458, 467)]] I will write the response. length 6 spans [[(559, 565), (567, 573), (575, 581), (583, 589), (591, 597), (599, 605), (607, 613), (615, 621)], []] Wait, the angle is in the third quadrant. Both sine and cosine are ne length 22 spans [[], [(470, 492), (495, 517), (520, 542), (545, 567)]]Cross-context repetition-associated neurons (score, layer, neuron, generation delta, within-text delta, agreement, individual deltas):(0.5012192130088806, 23, 22, 1.3447265625, 3.259765625, 1.0, [4.2578125, 3.029296875, 3.18359375, 2.2578125, 3.5703125])(0.46885350346565247, 23, 40, -1.13671875, -4.1875, 1.0, [-4.3359375, -3.552734375, -5.1640625, -4.05078125, -3.83984375])(0.4125770032405853, 23, 46, 0.962890625, 1.7958984375, 1.0, [3.1953125, 2.123046875, 1.94140625, 0.8291015625, 0.89013671875])(0.33822453022003174, 19, 11, -0.95556640625, -1.5712890625, 1.0, [-2.416015625, -2.244140625, -1.306640625, -1.009765625, -0.8798828125])(0.3285178244113922, 23, 94, 0.55322265625, 2.177734375, 1.0, [2.0234375, 1.818359375, 2.62890625, 2.591796875, 1.822265625])(0.30333033204078674, 18, 8, -0.70361328125, -1.509765625, 1.0, [-1.763671875, -1.4814453125, -1.2607421875, -1.185546875, -1.85546875])(0.2964679002761841, 23, 19, 0.599609375, 1.96875, 1.0, [1.9931640625, 2.154296875, 2.76953125, 1.90234375, 1.025390625])(0.2360299676656723, 22, 59, 0.5908203125, 1.1298828125, 1.0, [1.708984375, 1.138671875, 1.1376953125, 0.7080078125, 0.955078125])(0.1923191398382187, 22, 11, -0.72265625, -1.8837890625, 1.0, [-1.74609375, -1.45703125, -1.90234375, -2.19140625, -2.12109375])(0.185051828622818, 23, 350, -0.371826171875, -0.97216796875, 1.0, [-1.072265625, -0.916015625, -0.94189453125, -0.86376953125, -1.0673828125])(0.1638759970664978, 22, 89, -0.379150390625, -0.94140625, 1.0, [-1.4140625, -0.8828125, -0.9248046875, -0.56689453125, -0.91796875])(0.1326119601726532, 22, 38, -0.294921875, -0.88037109375, 1.0, [-0.8017578125, -1.27734375, -0.98291015625, -0.64208984375, -0.6982421875])(0.1282532960176468, 18, 29, 0.28759765625, 0.77685546875, 1.0, [1.029296875, 0.576171875, 0.9599609375, 0.55419921875, 0.76611328125])(0.12756240367889404, 22, 20, 0.305419921875, 0.8916015625, 1.0, [0.8310546875, 0.86181640625, 0.85986328125, 1.1171875, 0.7890625])(0.12283069640398026, 22, 7, 0.41748046875, 0.50634765625, 1.0, [0.93603515625, 0.406982421875, 0.39794921875, 0.2020263671875, 0.5888671875])(0.11640322208404541, 19, 1, 0.2607421875, 0.75830078125, 1.0, [0.6162109375, 0.744140625, 1.2236328125, 0.7060546875, 0.501953125])(0.11629579961299896, 15, 0, 0.35546875, 0.5869140625, 1.0, [0.6982421875, 0.75390625, 0.471923828125, 0.56982421875, 0.44189453125])(0.11597020924091339, 21, 30, -0.382080078125, -0.4892578125, 1.0, [-0.9482421875, -0.9150390625, -0.22705078125, -0.08197021484375, -0.27392578125])(0.10994921624660492, 19, 171, -0.220947265625, -0.49462890625, 1.0, [-0.65380859375, -0.5458984375, -0.6220703125, -0.1190185546875, -0.5322265625])(0.10250584036111832, 18, 32, 0.225830078125, 0.59033203125, 1.0, [0.73486328125, 0.77197265625, 0.407958984375, 0.408935546875, 0.6298828125])(0.10077561438083649, 20, 89, 0.178466796875, 0.5263671875, 1.0, [0.69189453125, 0.37890625, 0.5341796875, 0.330078125, 0.69677734375])(0.09288061857223512, 17, 20, 0.38525390625, 0.71044921875, 0.8, [1.537109375, 0.97802734375, 0.20703125, -0.2919921875, 1.1220703125])(0.08757990598678589, 12, 1226, 0.1986083984375, 0.611328125, 1.0, [0.52197265625, 0.60595703125, 0.8798828125, 0.80517578125, 0.2430419921875])(0.08523226529359818, 20, 77, 0.19921875, 0.58837890625, 1.0, [0.60791015625, 0.43505859375, 0.7001953125, 0.658203125, 0.541015625])(0.08261194825172424, 18, 106, 0.2451171875, 0.379638671875, 1.0, [0.6328125, 0.5390625, 0.27099609375, 0.10699462890625, 0.34912109375])(0.07991641014814377, 18, 1, -0.237060546875, -0.615234375, 1.0, [-0.40087890625, -0.47998046875, -0.6201171875, -0.94580078125, -0.6298828125])(0.07956857234239578, 14, 390, 0.172119140625, 0.5654296875, 1.0, [0.4970703125, 0.52685546875, 0.73779296875, 0.669921875, 0.39501953125])(0.07937994599342346, 18, 64, -0.1790771484375, -0.4970703125, 1.0, [-0.56982421875, -0.337890625, -0.5322265625, -0.5068359375, -0.5390625])(0.07718387991189957, 13, 7, -0.2509765625, -0.5087890625, 1.0, [-0.7431640625, -0.5888671875, -0.458984375, -0.23095703125, -0.52197265625])(0.07632462680339813, 14, 239, -0.2091064453125, -0.470947265625, 1.0, [-0.751953125, -0.3740234375, -0.323974609375, -0.18994140625, -0.7138671875])

python

# Hypothesis: the highest-confidence repetition neurons should push loop-related language rather than merely signal that reasoning is complete.import os,json,math,collectionsfrom pathlib import Pathimport numpy as npimport torchtorch.set_num_threads(8)gen1=[json.loads(s) for s in open("/work/gen_1.jsonl")]flat1=[dict(id=r["id"],source=r["source"],answer=r["answer"],**s) for r in gen1 for s in r["samples"]]idx1=json.load(open("/work/gen_1_acts/index.json"))M1=np.load("/work/gen_1_acts/mlp_mean_answer.npy").astype(np.float32)y1=np.array([s["correct"] for s in idx1["samples"]],bool)mean_bad=M1[~y1].mean(0); mean_good=M1[y1].mean(0)def ids_of(s):    r=tok.encode(s)    return list(r.ids) if hasattr(r,"ids") else list(r)candidates={18:[8],19:[11],22:[59,11,89],23:[22,40,46,94,19,350]}E=load_original("model.language_model.embed_tokens.weight")nf=load_original("model.language_model.norm.weight")print("Final norm (read only) range/median:",float(nf.min()),float(nf.median()),float(nf.max()))safe_words=[" Wait","Wait"," wait"," But","But"," However"," Therefore"," Thus"," So",            " again"," check"," repeat"," repeating"," loop"," answer"," response"," write",            " calculate"," equals"," because"," verify"," correct"," mistake"," Let's",            " I","1","2","0","+","=","\\","<think>","</think>","<|im_end|>"]safe_ids=sorted(set(t for s in safe_words for t in ids_of(s)))safe_labels={t:s for s in safe_words for t in ids_of(s) if len(ids_of(s))==1}# This is a static weight-to-vocabulary attribution, not a simulated forward pass.down_cache={}for l,js in candidates.items():    W=load_original(f"model.language_model.layers.{l}.mlp.down_proj.weight")    down_cache[l]=W    for j in js:        direction=W[:,j]*float(np.sign(mean_bad[l-1,j]-mean_good[l-1,j]))        scores=(E[safe_ids]@direction).numpy()        order=np.argsort(scores)        print((l,j),"loop/good mean",np.round([mean_bad[l-1,j],mean_good[l-1,j]],3).tolist(),              "positive",[(safe_labels.get(safe_ids[k],safe_ids[k]),round(float(scores[k]),4)) for k in order[-5:][::-1]],              "negative",[(safe_labels.get(safe_ids[k],safe_ids[k]),round(float(scores[k]),4)) for k in order[:4]])notes="""# Math weight-edit experimentBaseline pass 1: 256 original generations (96 GSM8K,160 MATH), full 32768-token limit.Accuracy 24/256=9.375%; 232 cutoffs (91.41%). Correct GSM=12/96, MATH=12/160.Failures enter exact repetitive thinking; 213/232 failed tails contain 'Wait,'.Repeated 12-token fractions first2048/middle/tail: failures .070/.506/.975, correct .039/.098/.177.Pass 2 (files fwd_3 after one uncounted attention-instrumentation error): two teacher-forced arithmetic/trigonometric probes, matched identical sentences at first vs repeated occurrences.Most consistent within-content repetition-associated MLP neurons:23:22 (+3.26),40 (-4.19),46 (+1.80),94 (+2.18),19 (+1.97);19:11 (-1.57);18:8 (-1.51);22:59 (+1.13).These signs agree across five distinct repetitive phrases and with failed-vs-correct generation averages.Caveat: teacher-forced chat template inserted empty thinking before supplied think; use raw templated text for follow-up.No edits tested yet. Eight forward passes remain. Need causal dose tests and independent paired validation."""Path("/work/notes.md").write_text(notes)print("Notes saved. Existing edits:",edited())

# Hypothesis: the highest-confidence repetition neurons should push loop-related language rather than merely signal that reasoning is complete.import os,json,math,collectionsfrom pathlib import Pathimport numpy as npimport torchtorch.set_num_threads(8)gen1=[json.loads(s) for s in open("/work/gen_1.jsonl")]flat1=[dict(id=r["id"],source=r["source"],answer=r["answer"],**s) for r in gen1 for s in r["samples"]]idx1=json.load(open("/work/gen_1_acts/index.json"))M1=np.load("/work/gen_1_acts/mlp_mean_answer.npy").astype(np.float32)y1=np.array([s["correct"] for s in idx1["samples"]],bool)mean_bad=M1[~y1].mean(0); mean_good=M1[y1].mean(0)def ids_of(s):    r=tok.encode(s)    return list(r.ids) if hasattr(r,"ids") else list(r)candidates={18:[8],19:[11],22:[59,11,89],23:[22,40,46,94,19,350]}E=load_original("model.language_model.embed_tokens.weight")nf=load_original("model.language_model.norm.weight")print("Final norm (read only) range/median:",float(nf.min()),float(nf.median()),float(nf.max()))safe_words=[" Wait","Wait"," wait"," But","But"," However"," Therefore"," Thus"," So",            " again"," check"," repeat"," repeating"," loop"," answer"," response"," write",            " calculate"," equals"," because"," verify"," correct"," mistake"," Let's",            " I","1","2","0","+","=","\\","<think>","</think>","<|im_end|>"]safe_ids=sorted(set(t for s in safe_words for t in ids_of(s)))safe_labels={t:s for s in safe_words for t in ids_of(s) if len(ids_of(s))==1}# This is a static weight-to-vocabulary attribution, not a simulated forward pass.down_cache={}for l,js in candidates.items():    W=load_original(f"model.language_model.layers.{l}.mlp.down_proj.weight")    down_cache[l]=W    for j in js:        direction=W[:,j]*float(np.sign(mean_bad[l-1,j]-mean_good[l-1,j]))        scores=(E[safe_ids]@direction).numpy()        order=np.argsort(scores)        print((l,j),"loop/good mean",np.round([mean_bad[l-1,j],mean_good[l-1,j]],3).tolist(),              "positive",[(safe_labels.get(safe_ids[k],safe_ids[k]),round(float(scores[k]),4)) for k in order[-5:][::-1]],              "negative",[(safe_labels.get(safe_ids[k],safe_ids[k]),round(float(scores[k]),4)) for k in order[:4]])notes="""# Math weight-edit experimentBaseline pass 1: 256 original generations (96 GSM8K,160 MATH), full 32768-token limit.Accuracy 24/256=9.375%; 232 cutoffs (91.41%). Correct GSM=12/96, MATH=12/160.Failures enter exact repetitive thinking; 213/232 failed tails contain 'Wait,'.Repeated 12-token fractions first2048/middle/tail: failures .070/.506/.975, correct .039/.098/.177.Pass 2 (files fwd_3 after one uncounted attention-instrumentation error): two teacher-forced arithmetic/trigonometric probes, matched identical sentences at first vs repeated occurrences.Most consistent within-content repetition-associated MLP neurons:23:22 (+3.26),40 (-4.19),46 (+1.80),94 (+2.18),19 (+1.97);19:11 (-1.57);18:8 (-1.51);22:59 (+1.13).These signs agree across five distinct repetitive phrases and with failed-vs-correct generation averages.Caveat: teacher-forced chat template inserted empty thinking before supplied think; use raw templated text for follow-up.No edits tested yet. Eight forward passes remain. Need causal dose tests and independent paired validation."""Path("/work/notes.md").write_text(notes)print("Notes saved. Existing edits:",edited())

OUTPUT of Python run 9 of at most 300:

Final norm (read only) range/median: -0.77734375 3.46875 5.3125(18, 8) loop/good mean [-0.6790000200271606, 0.02500000037252903] positive [('<|im_end|>', 0.0097), (579, 0.0066), (' because', 0.0062), ('=', 0.0062), (' repeating', 0.0053)] negative [('2', -0.0051), ('Wait', -0.0051), ('1', -0.0037), (' Wait', -0.0026)](19, 11) loop/good mean [-1.1929999589920044, -0.23800000548362732] positive [('2', 0.013), ('0', 0.0122), ('1', 0.0121), ('+', 0.0074), ('\\', 0.0072)] negative [(' response', -0.015), (' mistake', -0.0083), (' But', -0.0054), (' answer', -0.0054)](22, 59) loop/good mean [0.8230000138282776, 0.23199999332427979] positive [('1', 0.0147), ('0', 0.014), ('2', 0.0135), (' I', 0.0115), ('<|im_end|>', 0.0109)] negative [(' mistake', -0.0149), (' equals', -0.0106), (' again', -0.0104), (' calculate', -0.0071)](22, 11) loop/good mean [-5.9029998779296875, -5.179999828338623] positive [(' because', 0.008), (' Thus', 0.0076), (' correct', 0.007), (' Therefore', 0.0067), (' response', 0.0063)] negative [('2', -0.0099), ('1', -0.009), ('0', -0.0083), ('=', -0.0068)](22, 89) loop/good mean [-0.33899998664855957, 0.03999999910593033] positive [('</think>', 0.0023), ('<think>', 0.0016), (' Thus', 0.0015), (' verify', 0.0012), (' loop', 0.0007)] negative [('1', -0.0119), (' because', -0.0119), (' again', -0.0118), (' However', -0.0116)](23, 22) loop/good mean [1.50600004196167, 0.16099999845027924] positive [('<|im_end|>', 0.0019), ('</think>', -0.0017), (' verify', -0.0034), ('=', -0.0038), (' I', -0.004)] negative [(' However', -0.014), (' Therefore', -0.0134), ('\\', -0.0105), (6558, -0.0103)](23, 40) loop/good mean [-2.5980000495910645, -1.4609999656677246] positive [('=', 0.0023), ('+', 0.0004), ('</think>', 0.0003), ('<|im_end|>', -0.0003), (' mistake', -0.0009)] negative [(' However', -0.0092), (' loop', -0.0088), (' check', -0.0087), (' repeat', -0.0087)](23, 46) loop/good mean [3.240999937057495, 2.2799999713897705] positive [('<|im_end|>', 0.0163), (' because', 0.01), (' I', 0.0092), (579, 0.0085), (' So', 0.008)] negative [(' answer', -0.0012), ('Wait', -0.0009), (' verify', 0.0013), (' wait', 0.0016)](23, 94) loop/good mean [1.0049999952316284, 0.4519999921321869] positive [('<|im_end|>', 0.0204), (' because', 0.0129), (579, 0.0117), (' mistake', 0.009), (' again', 0.0076)] negative [(' Wait', -0.0078), ('2', -0.0077), ('Wait', -0.0072), (' wait', -0.0064)](23, 19) loop/good mean [0.7559999823570251, 0.15700000524520874] positive [(' repeat', 0.0061), (' equals', 0.0042), (' repeating', 0.0025), (' check', 0.0019), (' response', 0.0013)] negative [('</think>', -0.0114), (' However', -0.0113), (' I', -0.0109), (' again', -0.0105)](23, 350) loop/good mean [-0.7649999856948853, -0.3930000066757202] positive [(' write', 0.0236), (' check', 0.0216), (' response', 0.0208), (' But', 0.0199), ('But', 0.0199)] negative [('<think>', -0.0101), ('</think>', -0.0069), (' repeating', 0.0034), (' mistake', 0.0041)]Notes saved. Existing edits: []

Final norm (read only) range/median: -0.77734375 3.46875 5.3125(18, 8) loop/good mean [-0.6790000200271606, 0.02500000037252903] positive [('<|im_end|>', 0.0097), (579, 0.0066), (' because', 0.0062), ('=', 0.0062), (' repeating', 0.0053)] negative [('2', -0.0051), ('Wait', -0.0051), ('1', -0.0037), (' Wait', -0.0026)](19, 11) loop/good mean [-1.1929999589920044, -0.23800000548362732] positive [('2', 0.013), ('0', 0.0122), ('1', 0.0121), ('+', 0.0074), ('\\', 0.0072)] negative [(' response', -0.015), (' mistake', -0.0083), (' But', -0.0054), (' answer', -0.0054)](22, 59) loop/good mean [0.8230000138282776, 0.23199999332427979] positive [('1', 0.0147), ('0', 0.014), ('2', 0.0135), (' I', 0.0115), ('<|im_end|>', 0.0109)] negative [(' mistake', -0.0149), (' equals', -0.0106), (' again', -0.0104), (' calculate', -0.0071)](22, 11) loop/good mean [-5.9029998779296875, -5.179999828338623] positive [(' because', 0.008), (' Thus', 0.0076), (' correct', 0.007), (' Therefore', 0.0067), (' response', 0.0063)] negative [('2', -0.0099), ('1', -0.009), ('0', -0.0083), ('=', -0.0068)](22, 89) loop/good mean [-0.33899998664855957, 0.03999999910593033] positive [('</think>', 0.0023), ('<think>', 0.0016), (' Thus', 0.0015), (' verify', 0.0012), (' loop', 0.0007)] negative [('1', -0.0119), (' because', -0.0119), (' again', -0.0118), (' However', -0.0116)](23, 22) loop/good mean [1.50600004196167, 0.16099999845027924] positive [('<|im_end|>', 0.0019), ('</think>', -0.0017), (' verify', -0.0034), ('=', -0.0038), (' I', -0.004)] negative [(' However', -0.014), (' Therefore', -0.0134), ('\\', -0.0105), (6558, -0.0103)](23, 40) loop/good mean [-2.5980000495910645, -1.4609999656677246] positive [('=', 0.0023), ('+', 0.0004), ('</think>', 0.0003), ('<|im_end|>', -0.0003), (' mistake', -0.0009)] negative [(' However', -0.0092), (' loop', -0.0088), (' check', -0.0087), (' repeat', -0.0087)](23, 46) loop/good mean [3.240999937057495, 2.2799999713897705] positive [('<|im_end|>', 0.0163), (' because', 0.01), (' I', 0.0092), (579, 0.0085), (' So', 0.008)] negative [(' answer', -0.0012), ('Wait', -0.0009), (' verify', 0.0013), (' wait', 0.0016)](23, 94) loop/good mean [1.0049999952316284, 0.4519999921321869] positive [('<|im_end|>', 0.0204), (' because', 0.0129), (579, 0.0117), (' mistake', 0.009), (' again', 0.0076)] negative [(' Wait', -0.0078), ('2', -0.0077), ('Wait', -0.0072), (' wait', -0.0064)](23, 19) loop/good mean [0.7559999823570251, 0.15700000524520874] positive [(' repeat', 0.0061), (' equals', 0.0042), (' repeating', 0.0025), (' check', 0.0019), (' response', 0.0013)] negative [('</think>', -0.0114), (' However', -0.0113), (' I', -0.0109), (' again', -0.0105)](23, 350) loop/good mean [-0.7649999856948853, -0.3930000066757202] positive [(' write', 0.0236), (' check', 0.0216), (' response', 0.0208), (' But', 0.0199), ('But', 0.0199)] negative [('<think>', -0.0101), ('</think>', -0.0069), (' repeating', 0.0034), (' mistake', 0.0041)]Notes saved. Existing edits: []

python

# Hypothesis: four repetition-amplifying MLP neurons form a small feedback circuit; removing their output should interrupt loops without suppressing thinking generally.print(Path("/work/notes.md").read_text()[-4500:] if Path("/work/notes.md").exists() else "No previous notes.")targets = {18:[8], 19:[11], 23:[22,40]}def apply_circuit_strength(strength):    for name in list(edited()):        drop_edit(name)    changes=[]    for l,js in targets.items():        name=f"model.language_model.layers.{l}.mlp.down_proj.weight"        w=load_original(name)        new=w.clone()        new[:,js]*=(1-strength)        save_edit(name,new)        changes.append((l,js,float((new-w).norm()/w.norm())))    return changesprint("Ablation strength 1.0:",apply_circuit_strength(1.0))# Development set is fixed before observing intervention outcomes: first 32 GSM8K and first 64 MATH diagnostic items.dev_ids=diag_ids[:32]+diag_ids[96:160]print("Development IDs:",json.dumps(dev_ids))note = """## Diagnosis and first causal testBaseline: 256 original generations, accuracy 9.375%, 232/256 cut at 32768 tokens; GSM 12.5%, MATH 7.5%.Failed answers' last-4096-token repeated 12-gram fraction = 0.975; correct answers = 0.177.213/232 failures repeat 'Wait,' in their tails. Many have already solved the arithmetic before looping.Teacher-forced pass (files fwd_3; first failed attention request was not counted) used repeated identical sentences.Within-text repetition grows in the same direction as failure-vs-success activation contrast at neurons18:8, 19:11, 23:22, 23:40 across all five repeated contents. These are not selected merely by grades.Caveat: chat rendering inserted an extra empty think pair; subsequent dense probes should use raw texts.Current experimental edit: zero only down_proj columns 18:8, 19:11, 23:22 and 23:40.This is a targeted causal test, not yet a confirmed best edit. No protected tensors changed.Development set: first 32 GSM + first 64 MATH from diagnostic sample (96 problems).Budget: 2 successful forward passes consumed; 8 remain."""with open("/work/notes.md","a") as f: f.write(note)

# Hypothesis: four repetition-amplifying MLP neurons form a small feedback circuit; removing their output should interrupt loops without suppressing thinking generally.print(Path("/work/notes.md").read_text()[-4500:] if Path("/work/notes.md").exists() else "No previous notes.")targets = {18:[8], 19:[11], 23:[22,40]}def apply_circuit_strength(strength):    for name in list(edited()):        drop_edit(name)    changes=[]    for l,js in targets.items():        name=f"model.language_model.layers.{l}.mlp.down_proj.weight"        w=load_original(name)        new=w.clone()        new[:,js]*=(1-strength)        save_edit(name,new)        changes.append((l,js,float((new-w).norm()/w.norm())))    return changesprint("Ablation strength 1.0:",apply_circuit_strength(1.0))# Development set is fixed before observing intervention outcomes: first 32 GSM8K and first 64 MATH diagnostic items.dev_ids=diag_ids[:32]+diag_ids[96:160]print("Development IDs:",json.dumps(dev_ids))note = """## Diagnosis and first causal testBaseline: 256 original generations, accuracy 9.375%, 232/256 cut at 32768 tokens; GSM 12.5%, MATH 7.5%.Failed answers' last-4096-token repeated 12-gram fraction = 0.975; correct answers = 0.177.213/232 failures repeat 'Wait,' in their tails. Many have already solved the arithmetic before looping.Teacher-forced pass (files fwd_3; first failed attention request was not counted) used repeated identical sentences.Within-text repetition grows in the same direction as failure-vs-success activation contrast at neurons18:8, 19:11, 23:22, 23:40 across all five repeated contents. These are not selected merely by grades.Caveat: chat rendering inserted an extra empty think pair; subsequent dense probes should use raw texts.Current experimental edit: zero only down_proj columns 18:8, 19:11, 23:22 and 23:40.This is a targeted causal test, not yet a confirmed best edit. No protected tensors changed.Development set: first 32 GSM + first 64 MATH from diagnostic sample (96 problems).Budget: 2 successful forward passes consumed; 8 remain."""with open("/work/notes.md","a") as f: f.write(note)

OUTPUT of Python run 10 of at most 300:

# Math weight-edit experimentBaseline pass 1: 256 original generations (96 GSM8K,160 MATH), full 32768-token limit.Accuracy 24/256=9.375%; 232 cutoffs (91.41%). Correct GSM=12/96, MATH=12/160.Failures enter exact repetitive thinking; 213/232 failed tails contain 'Wait,'.Repeated 12-token fractions first2048/middle/tail: failures .070/.506/.975, correct .039/.098/.177.Pass 2 (files fwd_3 after one uncounted attention-instrumentation error): two teacher-forced arithmetic/trigonometric probes, matched identical sentences at first vs repeated occurrences.Most consistent within-content repetition-associated MLP neurons:23:22 (+3.26),40 (-4.19),46 (+1.80),94 (+2.18),19 (+1.97);19:11 (-1.57);18:8 (-1.51);22:59 (+1.13).These signs agree across five distinct repetitive phrases and with failed-vs-correct generation averages.Caveat: teacher-forced chat template inserted empty thinking before supplied think; use raw templated text for follow-up.No edits tested yet. Eight forward passes remain. Need causal dose tests and independent paired validation.
Ablation strength 1.0: [(18, [8], 0.017899204045534134), (19, [11], 0.015992557629942894), (23, [22, 40], 0.019595080986618996)]Traceback (most recent call last):  File "<string>", line 17, in <module>  File "<run>", line 18, in <module>NameError: name 'diag_ids' is not defined

# Math weight-edit experimentBaseline pass 1: 256 original generations (96 GSM8K,160 MATH), full 32768-token limit.Accuracy 24/256=9.375%; 232 cutoffs (91.41%). Correct GSM=12/96, MATH=12/160.Failures enter exact repetitive thinking; 213/232 failed tails contain 'Wait,'.Repeated 12-token fractions first2048/middle/tail: failures .070/.506/.975, correct .039/.098/.177.Pass 2 (files fwd_3 after one uncounted attention-instrumentation error): two teacher-forced arithmetic/trigonometric probes, matched identical sentences at first vs repeated occurrences.Most consistent within-content repetition-associated MLP neurons:23:22 (+3.26),40 (-4.19),46 (+1.80),94 (+2.18),19 (+1.97);19:11 (-1.57);18:8 (-1.51);22:59 (+1.13).These signs agree across five distinct repetitive phrases and with failed-vs-correct generation averages.Caveat: teacher-forced chat template inserted empty thinking before supplied think; use raw templated text for follow-up.No edits tested yet. Eight forward passes remain. Need causal dose tests and independent paired validation.
Ablation strength 1.0: [(18, [8], 0.017899204045534134), (19, [11], 0.015992557629942894), (23, [22, 40], 0.019595080986618996)]Traceback (most recent call last):  File "<string>", line 17, in <module>  File "<run>", line 18, in <module>NameError: name 'diag_ids' is not defined

python

# Hypothesis: suppressing four repetition-amplifying MLP neurons should break the feedback loop while preserving the mathematical reasoning circuit.import os, json, collections, mathfrom pathlib import Pathimport numpy as npimport torchprint("Current edits:", edited())print("Work files:", sorted(os.listdir("/work")))if Path("/work/notes.md").exists():    print(Path("/work/notes.md").read_text()[-3500:])gen1 = [json.loads(line) for line in open("/work/gen_1.jsonl")]flat1 = [dict(id=r["id"],source=r["source"],answer=r["answer"],**s) for r in gen1 for s in r["samples"]]diag_ids = [r["id"] for r in gen1]practice = {}for file in ["gsm8k_train.jsonl","math_train.jsonl","aime.jsonl"]:    practice[file] = [json.loads(line) for line in open("/practice/"+file)]by_id = {r["id"]:r for rows in practice.values() for r in rows}for name in edited():    a,b=load(name),load_original(name)    changed=(a!=b).any(0) if a.ndim==2 else (a!=b)    print(name,"changed columns/entries:",torch.where(changed)[0].tolist()[:30],          "relative change:",float((a-b).norm()/b.norm()))print("First 64 diagnostic IDs:",json.dumps(diag_ids[:32]+diag_ids[96:128]))

# Hypothesis: suppressing four repetition-amplifying MLP neurons should break the feedback loop while preserving the mathematical reasoning circuit.import os, json, collections, mathfrom pathlib import Pathimport numpy as npimport torchprint("Current edits:", edited())print("Work files:", sorted(os.listdir("/work")))if Path("/work/notes.md").exists():    print(Path("/work/notes.md").read_text()[-3500:])gen1 = [json.loads(line) for line in open("/work/gen_1.jsonl")]flat1 = [dict(id=r["id"],source=r["source"],answer=r["answer"],**s) for r in gen1 for s in r["samples"]]diag_ids = [r["id"] for r in gen1]practice = {}for file in ["gsm8k_train.jsonl","math_train.jsonl","aime.jsonl"]:    practice[file] = [json.loads(line) for line in open("/practice/"+file)]by_id = {r["id"]:r for rows in practice.values() for r in rows}for name in edited():    a,b=load(name),load_original(name)    changed=(a!=b).any(0) if a.ndim==2 else (a!=b)    print(name,"changed columns/entries:",torch.where(changed)[0].tolist()[:30],          "relative change:",float((a-b).norm()/b.norm()))print("First 64 diagnostic IDs:",json.dumps(diag_ids[:32]+diag_ids[96:128]))

OUTPUT of Python run 11 of at most 300:

Current edits: ['model.language_model.layers.18.mlp.down_proj.weight', 'model.language_model.layers.19.mlp.down_proj.weight', 'model.language_model.layers.23.mlp.down_proj.weight']Work files: ['edited', 'fwd_3', 'gen_1.jsonl', 'gen_1_acts', 'notes.md']# Math weight-edit experimentBaseline pass 1: 256 original generations (96 GSM8K,160 MATH), full 32768-token limit.Accuracy 24/256=9.375%; 232 cutoffs (91.41%). Correct GSM=12/96, MATH=12/160.Failures enter exact repetitive thinking; 213/232 failed tails contain 'Wait,'.Repeated 12-token fractions first2048/middle/tail: failures .070/.506/.975, correct .039/.098/.177.Pass 2 (files fwd_3 after one uncounted attention-instrumentation error): two teacher-forced arithmetic/trigonometric probes, matched identical sentences at first vs repeated occurrences.Most consistent within-content repetition-associated MLP neurons:23:22 (+3.26),40 (-4.19),46 (+1.80),94 (+2.18),19 (+1.97);19:11 (-1.57);18:8 (-1.51);22:59 (+1.13).These signs agree across five distinct repetitive phrases and with failed-vs-correct generation averages.Caveat: teacher-forced chat template inserted empty thinking before supplied think; use raw templated text for follow-up.No edits tested yet. Eight forward passes remain. Need causal dose tests and independent paired validation.
model.language_model.layers.18.mlp.down_proj.weight changed columns/entries: [8] relative change: 0.017899204045534134model.language_model.layers.19.mlp.down_proj.weight changed columns/entries: [11] relative change: 0.015992557629942894model.language_model.layers.23.mlp.down_proj.weight changed columns/entries: [22, 40] relative change: 0.019595080986618996First 64 diagnostic IDs: ["gsm8k_train_7354", "gsm8k_train_4960", "gsm8k_train_4335", "gsm8k_train_3553", "gsm8k_train_5789", "gsm8k_train_1381", "gsm8k_train_1927", "gsm8k_train_5240", "gsm8k_train_2157", "gsm8k_train_494", "gsm8k_train_7076", "gsm8k_train_6526", "gsm8k_train_422", "gsm8k_train_975", "gsm8k_train_1387", "gsm8k_train_5866", "gsm8k_train_462", "gsm8k_train_2739", "gsm8k_train_0", "gsm8k_train_5989", "gsm8k_train_1028", "gsm8k_train_714", "gsm8k_train_7181", "gsm8k_train_350", "gsm8k_train_7198", "gsm8k_train_3835", "gsm8k_train_2079", "gsm8k_train_5000", "gsm8k_train_7182", "gsm8k_train_2193", "gsm8k_train_576", "gsm8k_train_1098", "math_train_precalculus_587", "math_train_geometry_350", "math_train_intermediate_algebra_1136", "math_train_algebra_603", "math_train_prealgebra_68", "math_train_intermediate_algebra_895", "math_train_intermediate_algebra_497", "math_train_counting_and_probability_685", "math_train_algebra_635", "math_train_geometry_67", "math_train_prealgebra_321", "math_train_prealgebra_831", "math_train_algebra_469", "math_train_counting_and_probability_483", "math_train_algebra_241", "math_train_counting_and_probability_527", "math_train_counting_and_probability_644", "math_train_algebra_209", "math_train_intermediate_algebra_732", "math_train_number_theory_174", "math_train_algebra_1097", "math_train_intermediate_algebra_458", "math_train_algebra_317", "math_train_prealgebra_551", "math_train_prealgebra_1170", "math_train_algebra_1173", "math_train_number_theory_425", "math_train_counting_and_probability_666", "math_train_geometry_200", "math_train_precalculus_176", "math_train_intermediate_algebra_776", "math_train_counting_and_probability_278"]

Current edits: ['model.language_model.layers.18.mlp.down_proj.weight', 'model.language_model.layers.19.mlp.down_proj.weight', 'model.language_model.layers.23.mlp.down_proj.weight']Work files: ['edited', 'fwd_3', 'gen_1.jsonl', 'gen_1_acts', 'notes.md']# Math weight-edit experimentBaseline pass 1: 256 original generations (96 GSM8K,160 MATH), full 32768-token limit.Accuracy 24/256=9.375%; 232 cutoffs (91.41%). Correct GSM=12/96, MATH=12/160.Failures enter exact repetitive thinking; 213/232 failed tails contain 'Wait,'.Repeated 12-token fractions first2048/middle/tail: failures .070/.506/.975, correct .039/.098/.177.Pass 2 (files fwd_3 after one uncounted attention-instrumentation error): two teacher-forced arithmetic/trigonometric probes, matched identical sentences at first vs repeated occurrences.Most consistent within-content repetition-associated MLP neurons:23:22 (+3.26),40 (-4.19),46 (+1.80),94 (+2.18),19 (+1.97);19:11 (-1.57);18:8 (-1.51);22:59 (+1.13).These signs agree across five distinct repetitive phrases and with failed-vs-correct generation averages.Caveat: teacher-forced chat template inserted empty thinking before supplied think; use raw templated text for follow-up.No edits tested yet. Eight forward passes remain. Need causal dose tests and independent paired validation.
model.language_model.layers.18.mlp.down_proj.weight changed columns/entries: [8] relative change: 0.017899204045534134model.language_model.layers.19.mlp.down_proj.weight changed columns/entries: [11] relative change: 0.015992557629942894model.language_model.layers.23.mlp.down_proj.weight changed columns/entries: [22, 40] relative change: 0.019595080986618996First 64 diagnostic IDs: ["gsm8k_train_7354", "gsm8k_train_4960", "gsm8k_train_4335", "gsm8k_train_3553", "gsm8k_train_5789", "gsm8k_train_1381", "gsm8k_train_1927", "gsm8k_train_5240", "gsm8k_train_2157", "gsm8k_train_494", "gsm8k_train_7076", "gsm8k_train_6526", "gsm8k_train_422", "gsm8k_train_975", "gsm8k_train_1387", "gsm8k_train_5866", "gsm8k_train_462", "gsm8k_train_2739", "gsm8k_train_0", "gsm8k_train_5989", "gsm8k_train_1028", "gsm8k_train_714", "gsm8k_train_7181", "gsm8k_train_350", "gsm8k_train_7198", "gsm8k_train_3835", "gsm8k_train_2079", "gsm8k_train_5000", "gsm8k_train_7182", "gsm8k_train_2193", "gsm8k_train_576", "gsm8k_train_1098", "math_train_precalculus_587", "math_train_geometry_350", "math_train_intermediate_algebra_1136", "math_train_algebra_603", "math_train_prealgebra_68", "math_train_intermediate_algebra_895", "math_train_intermediate_algebra_497", "math_train_counting_and_probability_685", "math_train_algebra_635", "math_train_geometry_67", "math_train_prealgebra_321", "math_train_prealgebra_831", "math_train_algebra_469", "math_train_counting_and_probability_483", "math_train_algebra_241", "math_train_counting_and_probability_527", "math_train_counting_and_probability_644", "math_train_algebra_209", "math_train_intermediate_algebra_732", "math_train_number_theory_174", "math_train_algebra_1097", "math_train_intermediate_algebra_458", "math_train_algebra_317", "math_train_prealgebra_551", "math_train_prealgebra_1170", "math_train_algebra_1173", "math_train_number_theory_425", "math_train_counting_and_probability_666", "math_train_geometry_200", "math_train_precalculus_176", "math_train_intermediate_algebra_776", "math_train_counting_and_probability_278"]

forward

{"type":"generate","model":"both","problems":["gsm8k_train_7354","gsm8k_train_4960","gsm8k_train_4335","gsm8k_train_3553","gsm8k_train_5789","gsm8k_train_1381","gsm8k_train_1927","gsm8k_train_5240","gsm8k_train_2157","gsm8k_train_494","gsm8k_train_7076","gsm8k_train_6526","gsm8k_train_422","gsm8k_train_975","gsm8k_train_1387","gsm8k_train_5866","gsm8k_train_462","gsm8k_train_2739","gsm8k_train_0","gsm8k_train_5989","gsm8k_train_1028","gsm8k_train_714","gsm8k_train_7181","gsm8k_train_350","gsm8k_train_7198","gsm8k_train_3835","gsm8k_train_2079","gsm8k_train_5000","gsm8k_train_7182","gsm8k_train_2193","gsm8k_train_576","gsm8k_train_1098","math_train_precalculus_587","math_train_geometry_350","math_train_intermediate_algebra_1136","math_train_algebra_603","math_train_prealgebra_68","math_train_intermediate_algebra_895","math_train_intermediate_algebra_497","math_train_counting_and_probability_685","math_train_algebra_635","math_train_geometry_67","math_train_prealgebra_321","math_train_prealgebra_831","math_train_algebra_469","math_train_counting_and_probability_483","math_train_algebra_241","math_train_counting_and_probability_527","math_train_counting_and_probability_644","math_train_algebra_209","math_train_intermediate_algebra_732","math_train_number_theory_174","math_train_algebra_1097","math_train_intermediate_algebra_458","math_train_algebra_317","math_train_prealgebra_551","math_train_prealgebra_1170","math_train_algebra_1173","math_train_number_theory_425","math_train_counting_and_probability_666","math_train_geometry_200","math_train_precalculus_176","math_train_intermediate_algebra_776","math_train_counting_and_probability_278"],"n":2,"max_tokens":32768,"activations":{"layers":[18,19,23],"mlp":true}}

{"type":"generate","model":"both","problems":["gsm8k_train_7354","gsm8k_train_4960","gsm8k_train_4335","gsm8k_train_3553","gsm8k_train_5789","gsm8k_train_1381","gsm8k_train_1927","gsm8k_train_5240","gsm8k_train_2157","gsm8k_train_494","gsm8k_train_7076","gsm8k_train_6526","gsm8k_train_422","gsm8k_train_975","gsm8k_train_1387","gsm8k_train_5866","gsm8k_train_462","gsm8k_train_2739","gsm8k_train_0","gsm8k_train_5989","gsm8k_train_1028","gsm8k_train_714","gsm8k_train_7181","gsm8k_train_350","gsm8k_train_7198","gsm8k_train_3835","gsm8k_train_2079","gsm8k_train_5000","gsm8k_train_7182","gsm8k_train_2193","gsm8k_train_576","gsm8k_train_1098","math_train_precalculus_587","math_train_geometry_350","math_train_intermediate_algebra_1136","math_train_algebra_603","math_train_prealgebra_68","math_train_intermediate_algebra_895","math_train_intermediate_algebra_497","math_train_counting_and_probability_685","math_train_algebra_635","math_train_geometry_67","math_train_prealgebra_321","math_train_prealgebra_831","math_train_algebra_469","math_train_counting_and_probability_483","math_train_algebra_241","math_train_counting_and_probability_527","math_train_counting_and_probability_644","math_train_algebra_209","math_train_intermediate_algebra_732","math_train_number_theory_174","math_train_algebra_1097","math_train_intermediate_algebra_458","math_train_algebra_317","math_train_prealgebra_551","math_train_prealgebra_1170","math_train_algebra_1173","math_train_number_theory_425","math_train_counting_and_probability_666","math_train_geometry_200","math_train_precalculus_176","math_train_intermediate_algebra_776","math_train_counting_and_probability_278"],"n":2,"max_tokens":32768,"activations":{"layers":[18,19,23],"mlp":true}}

Forward pass 3 of 10 done (generate); results in /work/gen_4_original.jsonl and /work/gen_4_edited.jsonl. Summary:

{ "ok": true, "type": "generate", "model": "both", "files": {  "original": "/work/gen_4_original.jsonl",  "edited": "/work/gen_4_edited.jsonl" }, "activations": {  "original": {   "dir": "/work/gen_4_original_acts/",   "files": {    "hidden_last_prompt.npy": [     128,     3,     1024    ],    "hidden_mean_answer.npy": [     128,     3,     1024    ],    "mlp_last_prompt.npy": [     128,     3,     3584    ],    "mlp_mean_answer.npy": [     128,     3,     3584    ]   }  },  "edited": {   "dir": "/work/gen_4_edited_acts/",   "files": {    "hidden_last_prompt.npy": [     128,     3,     1024    ],    "hidden_mean_answer.npy": [     128,     3,     1024    ],    "mlp_last_prompt.npy": [     128,     3,     3584    ],    "mlp_mean_answer.npy": [     128,     3,     3584    ]   }  } }, "original": {  "generations": 128,  "graded": 128,  "accuracy": 0.1015625,  "accuracy_by_source": {   "gsm8k": 0.125,   "math": 0.0781  },  "mean_tokens": 30207,  "cut_share": 0.9062,  "failures": {   "wrong_answer": 1,   "no_answer_finished": 0,   "cut_at_max_tokens": 114  } }, "edited": {  "generations": 128,  "graded": 128,  "accuracy": 0.0859375,  "accuracy_by_source": {   "gsm8k": 0.1094,   "math": 0.0625  },  "mean_tokens": 30363,  "cut_share": 0.9141,  "failures": {   "wrong_answer": 0,   "no_answer_finished": 0,   "cut_at_max_tokens": 117  } }, "paired": {  "items": 64,  "accuracy_difference": -0.0156,  "standard_error": 0.0272,  "samples_wrong_to_right": 5,  "samples_right_to_wrong": 7 }, "s": 1039.9}

{ "ok": true, "type": "generate", "model": "both", "files": {  "original": "/work/gen_4_original.jsonl",  "edited": "/work/gen_4_edited.jsonl" }, "activations": {  "original": {   "dir": "/work/gen_4_original_acts/",   "files": {    "hidden_last_prompt.npy": [     128,     3,     1024    ],    "hidden_mean_answer.npy": [     128,     3,     1024    ],    "mlp_last_prompt.npy": [     128,     3,     3584    ],    "mlp_mean_answer.npy": [     128,     3,     3584    ]   }  },  "edited": {   "dir": "/work/gen_4_edited_acts/",   "files": {    "hidden_last_prompt.npy": [     128,     3,     1024    ],    "hidden_mean_answer.npy": [     128,     3,     1024    ],    "mlp_last_prompt.npy": [     128,     3,     3584    ],    "mlp_mean_answer.npy": [     128,     3,     3584    ]   }  } }, "original": {  "generations": 128,  "graded": 128,  "accuracy": 0.1015625,  "accuracy_by_source": {   "gsm8k": 0.125,   "math": 0.0781  },  "mean_tokens": 30207,  "cut_share": 0.9062,  "failures": {   "wrong_answer": 1,   "no_answer_finished": 0,   "cut_at_max_tokens": 114  } }, "edited": {  "generations": 128,  "graded": 128,  "accuracy": 0.0859375,  "accuracy_by_source": {   "gsm8k": 0.1094,   "math": 0.0625  },  "mean_tokens": 30363,  "cut_share": 0.9141,  "failures": {   "wrong_answer": 0,   "no_answer_finished": 0,   "cut_at_max_tokens": 117  } }, "paired": {  "items": 64,  "accuracy_difference": -0.0156,  "standard_error": 0.0272,  "samples_wrong_to_right": 5,  "samples_right_to_wrong": 7 }, "s": 1039.9}

Forward passes left: 7.

python

# Hypothesis: the first causal intervention did not improve accuracy; recover its exact weights and records before interpreting or revising it.print("Current edits:", edited())print("Work files:", sorted(os.listdir("/work")))notes_path=Path("/work/notes.md")if notes_path.exists():    print("Notes:", notes_path.read_text()[-7000:])for name in edited():    w=load(name); w0=load_original(name)    d=w-w0    print("EDIT", name, "relative norm",float(d.norm()/w0.norm()),"changed entries",int((d!=0).sum()))    if w.ndim==2:        cols=torch.nonzero((d!=0).any(0)).flatten()        print("Changed columns:",cols.tolist()[:100])        print("Column norm ratios:",[(int(j),round(float(w[:,j].norm()/w0[:,j].norm()),4)) for j in cols[:30]])print("Kernel variables present:", {k:k in globals() for k in ["flat1","TFM","repeat_deltas","dense_rows","meta3","diag_ids"]})for p in ["/work/gen_4_original_acts/index.json","/work/gen_4_edited_acts/index.json"]:    ix=json.load(open(p))    print(p, "layers",ix.get("layers"), "mlp_layers",ix.get("mlp_layers"),          "first samples",ix["samples"][:3])

# Hypothesis: the first causal intervention did not improve accuracy; recover its exact weights and records before interpreting or revising it.print("Current edits:", edited())print("Work files:", sorted(os.listdir("/work")))notes_path=Path("/work/notes.md")if notes_path.exists():    print("Notes:", notes_path.read_text()[-7000:])for name in edited():    w=load(name); w0=load_original(name)    d=w-w0    print("EDIT", name, "relative norm",float(d.norm()/w0.norm()),"changed entries",int((d!=0).sum()))    if w.ndim==2:        cols=torch.nonzero((d!=0).any(0)).flatten()        print("Changed columns:",cols.tolist()[:100])        print("Column norm ratios:",[(int(j),round(float(w[:,j].norm()/w0[:,j].norm()),4)) for j in cols[:30]])print("Kernel variables present:", {k:k in globals() for k in ["flat1","TFM","repeat_deltas","dense_rows","meta3","diag_ids"]})for p in ["/work/gen_4_original_acts/index.json","/work/gen_4_edited_acts/index.json"]:    ix=json.load(open(p))    print(p, "layers",ix.get("layers"), "mlp_layers",ix.get("mlp_layers"),          "first samples",ix["samples"][:3])

OUTPUT of Python run 12 of at most 300:

Current edits: ['model.language_model.layers.18.mlp.down_proj.weight', 'model.language_model.layers.19.mlp.down_proj.weight', 'model.language_model.layers.23.mlp.down_proj.weight']Work files: ['edited', 'fwd_3', 'gen_1.jsonl', 'gen_1_acts', 'gen_4_edited.jsonl', 'gen_4_edited_acts', 'gen_4_original.jsonl', 'gen_4_original_acts', 'notes.md']Notes: # Math weight-edit experimentBaseline pass 1: 256 original generations (96 GSM8K,160 MATH), full 32768-token limit.Accuracy 24/256=9.375%; 232 cutoffs (91.41%). Correct GSM=12/96, MATH=12/160.Failures enter exact repetitive thinking; 213/232 failed tails contain 'Wait,'.Repeated 12-token fractions first2048/middle/tail: failures .070/.506/.975, correct .039/.098/.177.Pass 2 (files fwd_3 after one uncounted attention-instrumentation error): two teacher-forced arithmetic/trigonometric probes, matched identical sentences at first vs repeated occurrences.Most consistent within-content repetition-associated MLP neurons:23:22 (+3.26),40 (-4.19),46 (+1.80),94 (+2.18),19 (+1.97);19:11 (-1.57);18:8 (-1.51);22:59 (+1.13).These signs agree across five distinct repetitive phrases and with failed-vs-correct generation averages.Caveat: teacher-forced chat template inserted empty thinking before supplied think; use raw templated text for follow-up.No edits tested yet. Eight forward passes remain. Need causal dose tests and independent paired validation.
EDIT model.language_model.layers.18.mlp.down_proj.weight relative norm 0.017899204045534134 changed entries 1024Changed columns: [8]Column norm ratios: [(8, 0.0)]EDIT model.language_model.layers.19.mlp.down_proj.weight relative norm 0.015992557629942894 changed entries 1024Changed columns: [11]Column norm ratios: [(11, 0.0)]EDIT model.language_model.layers.23.mlp.down_proj.weight relative norm 0.019595080986618996 changed entries 2048Changed columns: [22, 40]Column norm ratios: [(22, 0.0), (40, 0.0)]Kernel variables present: {'flat1': True, 'TFM': False, 'repeat_deltas': False, 'dense_rows': False, 'meta3': False, 'diag_ids': True}/work/gen_4_original_acts/index.json layers [18, 19, 23] mlp_layers [18, 19, 23] first samples [{'item': 'gsm8k_train_7354', 'sample': 0, 'correct': False, 'tokens': 8192}, {'item': 'gsm8k_train_7354', 'sample': 1, 'correct': False, 'tokens': 8192}, {'item': 'gsm8k_train_4960', 'sample': 0, 'correct': False, 'tokens': 8192}]/work/gen_4_edited_acts/index.json layers [18, 19, 23] mlp_layers [18, 19, 23] first samples [{'item': 'gsm8k_train_7354', 'sample': 0, 'correct': False, 'tokens': 8192}, {'item': 'gsm8k_train_7354', 'sample': 1, 'correct': False, 'tokens': 8192}, {'item': 'gsm8k_train_4960', 'sample': 0, 'correct': False, 'tokens': 8192}]

Current edits: ['model.language_model.layers.18.mlp.down_proj.weight', 'model.language_model.layers.19.mlp.down_proj.weight', 'model.language_model.layers.23.mlp.down_proj.weight']Work files: ['edited', 'fwd_3', 'gen_1.jsonl', 'gen_1_acts', 'gen_4_edited.jsonl', 'gen_4_edited_acts', 'gen_4_original.jsonl', 'gen_4_original_acts', 'notes.md']Notes: # Math weight-edit experimentBaseline pass 1: 256 original generations (96 GSM8K,160 MATH), full 32768-token limit.Accuracy 24/256=9.375%; 232 cutoffs (91.41%). Correct GSM=12/96, MATH=12/160.Failures enter exact repetitive thinking; 213/232 failed tails contain 'Wait,'.Repeated 12-token fractions first2048/middle/tail: failures .070/.506/.975, correct .039/.098/.177.Pass 2 (files fwd_3 after one uncounted attention-instrumentation error): two teacher-forced arithmetic/trigonometric probes, matched identical sentences at first vs repeated occurrences.Most consistent within-content repetition-associated MLP neurons:23:22 (+3.26),40 (-4.19),46 (+1.80),94 (+2.18),19 (+1.97);19:11 (-1.57);18:8 (-1.51);22:59 (+1.13).These signs agree across five distinct repetitive phrases and with failed-vs-correct generation averages.Caveat: teacher-forced chat template inserted empty thinking before supplied think; use raw templated text for follow-up.No edits tested yet. Eight forward passes remain. Need causal dose tests and independent paired validation.
EDIT model.language_model.layers.18.mlp.down_proj.weight relative norm 0.017899204045534134 changed entries 1024Changed columns: [8]Column norm ratios: [(8, 0.0)]EDIT model.language_model.layers.19.mlp.down_proj.weight relative norm 0.015992557629942894 changed entries 1024Changed columns: [11]Column norm ratios: [(11, 0.0)]EDIT model.language_model.layers.23.mlp.down_proj.weight relative norm 0.019595080986618996 changed entries 2048Changed columns: [22, 40]Column norm ratios: [(22, 0.0), (40, 0.0)]Kernel variables present: {'flat1': True, 'TFM': False, 'repeat_deltas': False, 'dense_rows': False, 'meta3': False, 'diag_ids': True}/work/gen_4_original_acts/index.json layers [18, 19, 23] mlp_layers [18, 19, 23] first samples [{'item': 'gsm8k_train_7354', 'sample': 0, 'correct': False, 'tokens': 8192}, {'item': 'gsm8k_train_7354', 'sample': 1, 'correct': False, 'tokens': 8192}, {'item': 'gsm8k_train_4960', 'sample': 0, 'correct': False, 'tokens': 8192}]/work/gen_4_edited_acts/index.json layers [18, 19, 23] mlp_layers [18, 19, 23] first samples [{'item': 'gsm8k_train_7354', 'sample': 0, 'correct': False, 'tokens': 8192}, {'item': 'gsm8k_train_7354', 'sample': 1, 'correct': False, 'tokens': 8192}, {'item': 'gsm8k_train_4960', 'sample': 0, 'correct': False, 'tokens': 8192}]

python

# Hypothesis: ablating the four repetition-associated MLP outputs should reduce repetitive cutoffs and improve paired accuracy, not merely shorten answers.import os, json, collections, mathfrom pathlib import Pathimport numpy as npimport torchdef read_generations(path):    rows=[json.loads(s) for s in open(path)]    return [dict(id=r["id"],source=r["source"],answer=r["answer"],sample=j,**s)            for r in rows for j,s in enumerate(r["samples"])]g4o=read_generations("/work/gen_4_original.jsonl")g4e=read_generations("/work/gen_4_edited.jsonl")for label,rs in [("original",g4o),("four-neuron ablation",g4e)]:    print(label,"N",len(rs),"correct",sum(r["correct"] for r in rs),          "cut",sum(r["cut"] for r in rs),"mean tokens",round(np.mean([r["tokens"] for r in rs])))    print("by source",[(s, sum(r["correct"] for r in rs if r["source"]==s),                       sum(r["source"]==s for r in rs)) for s in ["gsm8k","math"]])d=np.array([int(b["correct"])-int(a["correct"]) for a,b in zip(g4o,g4e)])grouped=collections.defaultdict(list)for r,v in zip(g4o,d): grouped[r["id"]].append(v)di=np.array([np.mean(v) for v in grouped.values()])print("Paired gain:",round(100*d.mean(),2),"pp; item-cluster SE",round(100*di.std(ddof=1)/np.sqrt(len(di)),2),      "pp; wrong-to-right",int((d==1).sum()),"right-to-wrong",int((d==-1).sum()))for i in [0,1,2,len(g4e)//2]:    r=g4e[i]    print("Edited excerpt",r["id"],"sample",r["sample"],"correct",r["correct"],"tokens",r["tokens"],          "tail",repr(r["text"][-240:]))print("Test item IDs:",json.dumps(list(grouped)))

# Hypothesis: ablating the four repetition-associated MLP outputs should reduce repetitive cutoffs and improve paired accuracy, not merely shorten answers.import os, json, collections, mathfrom pathlib import Pathimport numpy as npimport torchdef read_generations(path):    rows=[json.loads(s) for s in open(path)]    return [dict(id=r["id"],source=r["source"],answer=r["answer"],sample=j,**s)            for r in rows for j,s in enumerate(r["samples"])]g4o=read_generations("/work/gen_4_original.jsonl")g4e=read_generations("/work/gen_4_edited.jsonl")for label,rs in [("original",g4o),("four-neuron ablation",g4e)]:    print(label,"N",len(rs),"correct",sum(r["correct"] for r in rs),          "cut",sum(r["cut"] for r in rs),"mean tokens",round(np.mean([r["tokens"] for r in rs])))    print("by source",[(s, sum(r["correct"] for r in rs if r["source"]==s),                       sum(r["source"]==s for r in rs)) for s in ["gsm8k","math"]])d=np.array([int(b["correct"])-int(a["correct"]) for a,b in zip(g4o,g4e)])grouped=collections.defaultdict(list)for r,v in zip(g4o,d): grouped[r["id"]].append(v)di=np.array([np.mean(v) for v in grouped.values()])print("Paired gain:",round(100*d.mean(),2),"pp; item-cluster SE",round(100*di.std(ddof=1)/np.sqrt(len(di)),2),      "pp; wrong-to-right",int((d==1).sum()),"right-to-wrong",int((d==-1).sum()))for i in [0,1,2,len(g4e)//2]:    r=g4e[i]    print("Edited excerpt",r["id"],"sample",r["sample"],"correct",r["correct"],"tokens",r["tokens"],          "tail",repr(r["text"][-240:]))print("Test item IDs:",json.dumps(list(grouped)))

OUTPUT of Python run 13 of at most 300:

original N 128 correct 13 cut 116 mean tokens 30207by source [('gsm8k', 8, 64), ('math', 5, 64)]four-neuron ablation N 128 correct 11 cut 117 mean tokens 30363by source [('gsm8k', 7, 64), ('math', 4, 64)]Paired gain: -1.56 pp; item-cluster SE 2.72 pp; wrong-to-right 5 right-to-wrong 7Edited excerpt gsm8k_train_7354 sample 0 correct False tokens 32768 tail 'says "In May, June, July and August, she has to cut her lawn 4 times per month."\n        *   This implies she cuts 4 times in those months.\n        *   Wait, does "She cuts her lawn 4 times per month" mean she cuts 4 times *in all* months?\n'Edited excerpt gsm8k_train_7354 sample 1 correct False tokens 32768 tail ' she cut in April?\n            *   Wait, does she cut in March?\n            *   Wait, does she cut in May?\n            *   Wait, does she cut in June?\n            *   Wait, does she cut in July?\n            *   Wait, does she cut in August?'Edited excerpt gsm8k_train_4960 sample 0 correct False tokens 32768 tail 'Wait, if the waiter receives a 10% tip, does he add 10% of the bill to the bill?\n        *   Wait, if the waiter receives a 10% tip, does he add 10% of the bill to the bill?\n        *   Wait, if the waiter receives a 10% tip, does he add 10'Edited excerpt math_train_precalculus_587 sample 0 correct True tokens 1745 tail 'iply by 2:\n$$ 2 \\times \\frac{4}{9} = \\frac{8}{9} $$\n\nFinally, subtract 1 from the result:\n$$ \\frac{8}{9} - 1 = \\frac{8}{9} - \\frac{9}{9} = -\\frac{1}{9} $$\n\n**Conclusion:**\nThe value of $\\cos 2\\theta$ is $-\\frac{1}{9}$.\n\n\\boxed{-\\frac{1}{9}}'Test item IDs: ["gsm8k_train_7354", "gsm8k_train_4960", "gsm8k_train_4335", "gsm8k_train_3553", "gsm8k_train_5789", "gsm8k_train_1381", "gsm8k_train_1927", "gsm8k_train_5240", "gsm8k_train_2157", "gsm8k_train_494", "gsm8k_train_7076", "gsm8k_train_6526", "gsm8k_train_422", "gsm8k_train_975", "gsm8k_train_1387", "gsm8k_train_5866", "gsm8k_train_462", "gsm8k_train_2739", "gsm8k_train_0", "gsm8k_train_5989", "gsm8k_train_1028", "gsm8k_train_714", "gsm8k_train_7181", "gsm8k_train_350", "gsm8k_train_7198", "gsm8k_train_3835", "gsm8k_train_2079", "gsm8k_train_5000", "gsm8k_train_7182", "gsm8k_train_2193", "gsm8k_train_576", "gsm8k_train_1098", "math_train_precalculus_587", "math_train_geometry_350", "math_train_intermediate_algebra_1136", "math_train_algebra_603", "math_train_prealgebra_68", "math_train_intermediate_algebra_895", "math_train_intermediate_algebra_497", "math_train_counting_and_probability_685", "math_train_algebra_635", "math_train_geometry_67", "math_train_prealgebra_321", "math_train_prealgebra_831", "math_train_algebra_469", "math_train_counting_and_probability_483", "math_train_algebra_241", "math_train_counting_and_probability_527", "math_train_counting_and_probability_644", "math_train_algebra_209", "math_train_intermediate_algebra_732", "math_train_number_theory_174", "math_train_algebra_1097", "math_train_intermediate_algebra_458", "math_train_algebra_317", "math_train_prealgebra_551", "math_train_prealgebra_1170", "math_train_algebra_1173", "math_train_number_theory_425", "math_train_counting_and_probability_666", "math_train_geometry_200", "math_train_precalculus_176", "math_train_intermediate_algebra_776", "math_train_counting_and_probability_278"]

original N 128 correct 13 cut 116 mean tokens 30207by source [('gsm8k', 8, 64), ('math', 5, 64)]four-neuron ablation N 128 correct 11 cut 117 mean tokens 30363by source [('gsm8k', 7, 64), ('math', 4, 64)]Paired gain: -1.56 pp; item-cluster SE 2.72 pp; wrong-to-right 5 right-to-wrong 7Edited excerpt gsm8k_train_7354 sample 0 correct False tokens 32768 tail 'says "In May, June, July and August, she has to cut her lawn 4 times per month."\n        *   This implies she cuts 4 times in those months.\n        *   Wait, does "She cuts her lawn 4 times per month" mean she cuts 4 times *in all* months?\n'Edited excerpt gsm8k_train_7354 sample 1 correct False tokens 32768 tail ' she cut in April?\n            *   Wait, does she cut in March?\n            *   Wait, does she cut in May?\n            *   Wait, does she cut in June?\n            *   Wait, does she cut in July?\n            *   Wait, does she cut in August?'Edited excerpt gsm8k_train_4960 sample 0 correct False tokens 32768 tail 'Wait, if the waiter receives a 10% tip, does he add 10% of the bill to the bill?\n        *   Wait, if the waiter receives a 10% tip, does he add 10% of the bill to the bill?\n        *   Wait, if the waiter receives a 10% tip, does he add 10'Edited excerpt math_train_precalculus_587 sample 0 correct True tokens 1745 tail 'iply by 2:\n$$ 2 \\times \\frac{4}{9} = \\frac{8}{9} $$\n\nFinally, subtract 1 from the result:\n$$ \\frac{8}{9} - 1 = \\frac{8}{9} - \\frac{9}{9} = -\\frac{1}{9} $$\n\n**Conclusion:**\nThe value of $\\cos 2\\theta$ is $-\\frac{1}{9}$.\n\n\\boxed{-\\frac{1}{9}}'Test item IDs: ["gsm8k_train_7354", "gsm8k_train_4960", "gsm8k_train_4335", "gsm8k_train_3553", "gsm8k_train_5789", "gsm8k_train_1381", "gsm8k_train_1927", "gsm8k_train_5240", "gsm8k_train_2157", "gsm8k_train_494", "gsm8k_train_7076", "gsm8k_train_6526", "gsm8k_train_422", "gsm8k_train_975", "gsm8k_train_1387", "gsm8k_train_5866", "gsm8k_train_462", "gsm8k_train_2739", "gsm8k_train_0", "gsm8k_train_5989", "gsm8k_train_1028", "gsm8k_train_714", "gsm8k_train_7181", "gsm8k_train_350", "gsm8k_train_7198", "gsm8k_train_3835", "gsm8k_train_2079", "gsm8k_train_5000", "gsm8k_train_7182", "gsm8k_train_2193", "gsm8k_train_576", "gsm8k_train_1098", "math_train_precalculus_587", "math_train_geometry_350", "math_train_intermediate_algebra_1136", "math_train_algebra_603", "math_train_prealgebra_68", "math_train_intermediate_algebra_895", "math_train_intermediate_algebra_497", "math_train_counting_and_probability_685", "math_train_algebra_635", "math_train_geometry_67", "math_train_prealgebra_321", "math_train_prealgebra_831", "math_train_algebra_469", "math_train_counting_and_probability_483", "math_train_algebra_241", "math_train_counting_and_probability_527", "math_train_counting_and_probability_644", "math_train_algebra_209", "math_train_intermediate_algebra_732", "math_train_number_theory_174", "math_train_algebra_1097", "math_train_intermediate_algebra_458", "math_train_algebra_317", "math_train_prealgebra_551", "math_train_prealgebra_1170", "math_train_algebra_1173", "math_train_number_theory_425", "math_train_counting_and_probability_666", "math_train_geometry_200", "math_train_precalculus_176", "math_train_intermediate_algebra_776", "math_train_counting_and_probability_278"]

python

# Hypothesis: the four-neuron ablation did not causally repair repetition; recover experiment state before selecting a more structural intervention.print("Notes:")p=Path("/work/notes.md")print(p.read_text()[-7500:] if p.exists() else "No notes file.")print("Current edits:", edited())print("Work files:", sorted(os.listdir("/work")))print("Relevant state variables:", [k for k in list(globals()) if any(s in k.lower() for s in ["edit","ablat","test","candidate","repeat","score","embed","norm"]) and not k.startswith("_")])

# Hypothesis: the four-neuron ablation did not causally repair repetition; recover experiment state before selecting a more structural intervention.print("Notes:")p=Path("/work/notes.md")print(p.read_text()[-7500:] if p.exists() else "No notes file.")print("Current edits:", edited())print("Work files:", sorted(os.listdir("/work")))print("Relevant state variables:", [k for k in list(globals()) if any(s in k.lower() for s in ["edit","ablat","test","candidate","repeat","score","embed","norm"]) and not k.startswith("_")])

OUTPUT of Python run 14 of at most 300:

Notes:# Math weight-edit experimentBaseline pass 1: 256 original generations (96 GSM8K,160 MATH), full 32768-token limit.Accuracy 24/256=9.375%; 232 cutoffs (91.41%). Correct GSM=12/96, MATH=12/160.Failures enter exact repetitive thinking; 213/232 failed tails contain 'Wait,'.Repeated 12-token fractions first2048/middle/tail: failures .070/.506/.975, correct .039/.098/.177.Pass 2 (files fwd_3 after one uncounted attention-instrumentation error): two teacher-forced arithmetic/trigonometric probes, matched identical sentences at first vs repeated occurrences.Most consistent within-content repetition-associated MLP neurons:23:22 (+3.26),40 (-4.19),46 (+1.80),94 (+2.18),19 (+1.97);19:11 (-1.57);18:8 (-1.51);22:59 (+1.13).These signs agree across five distinct repetitive phrases and with failed-vs-correct generation averages.Caveat: teacher-forced chat template inserted empty thinking before supplied think; use raw templated text for follow-up.No edits tested yet. Eight forward passes remain. Need causal dose tests and independent paired validation.
Current edits: ['model.language_model.layers.18.mlp.down_proj.weight', 'model.language_model.layers.19.mlp.down_proj.weight', 'model.language_model.layers.23.mlp.down_proj.weight']Work files: ['edited', 'fwd_3', 'gen_1.jsonl', 'gen_1_acts', 'gen_4_edited.jsonl', 'gen_4_edited_acts', 'gen_4_original.jsonl', 'gen_4_original_acts', 'notes.md']Relevant state variables: ['save_edit', 'drop_edit', 'edited', 'candidates', 'scores']

Notes:# Math weight-edit experimentBaseline pass 1: 256 original generations (96 GSM8K,160 MATH), full 32768-token limit.Accuracy 24/256=9.375%; 232 cutoffs (91.41%). Correct GSM=12/96, MATH=12/160.Failures enter exact repetitive thinking; 213/232 failed tails contain 'Wait,'.Repeated 12-token fractions first2048/middle/tail: failures .070/.506/.975, correct .039/.098/.177.Pass 2 (files fwd_3 after one uncounted attention-instrumentation error): two teacher-forced arithmetic/trigonometric probes, matched identical sentences at first vs repeated occurrences.Most consistent within-content repetition-associated MLP neurons:23:22 (+3.26),40 (-4.19),46 (+1.80),94 (+2.18),19 (+1.97);19:11 (-1.57);18:8 (-1.51);22:59 (+1.13).These signs agree across five distinct repetitive phrases and with failed-vs-correct generation averages.Caveat: teacher-forced chat template inserted empty thinking before supplied think; use raw templated text for follow-up.No edits tested yet. Eight forward passes remain. Need causal dose tests and independent paired validation.
Current edits: ['model.language_model.layers.18.mlp.down_proj.weight', 'model.language_model.layers.19.mlp.down_proj.weight', 'model.language_model.layers.23.mlp.down_proj.weight']Work files: ['edited', 'fwd_3', 'gen_1.jsonl', 'gen_1_acts', 'gen_4_edited.jsonl', 'gen_4_edited_acts', 'gen_4_original.jsonl', 'gen_4_original_acts', 'notes.md']Relevant state variables: ['save_edit', 'drop_edit', 'edited', 'candidates', 'scores']

python

# Hypothesis: the targeted repetition-neuron ablation has produced a causal test; recover its exact strength and paired outcomes before choosing a dose.print("Edited tensors:", edited())for name in edited():    w=load(name); w0=load_original(name)    d=w-w0    cols=torch.nonzero(d.abs().sum(0)>0).flatten() if w.ndim==2 else torch.nonzero(d!=0).flatten()    print(name, "changed columns:",cols.tolist(),          "relative norm change:",round(float(d.norm()/w0.norm()),6))    if w.ndim==2:        print("column multipliers:",[(int(j),round(float((w[:,j]*w0[:,j]).sum()/w0[:,j].square().sum()),4)) for j in cols[:40]])for fn in ["gen_4_original.jsonl","gen_4_edited.jsonl"]:    p=Path("/work")/fn    if p.exists():        rows=[json.loads(s) for s in open(p)]        flat=[dict(id=r["id"],source=r["source"],**s) for r in rows for s in r["samples"]]        print(fn,"n",len(flat),"accuracy",np.mean([r["correct"] for r in flat]),              "cut",np.mean([r["cut"] for r in flat]),"mean tokens",np.mean([r["tokens"] for r in flat]))        print("by source",[(src,sum(r["correct"] for r in flat if r["source"]==src),sum(r["source"]==src for r in flat)) for src in ["gsm8k","math"]])        globals()[fn.split(".")[0]]=flatif "gen_4_original" in globals() and "gen_4_edited" in globals():    dd=np.array([int(e["correct"])-int(o["correct"]) for o,e in zip(gen_4_original,gen_4_edited)])    print("Paired gain",float(dd.mean()),"SE",float(dd.std(ddof=1)/np.sqrt(len(dd))),"rescues",int((dd>0).sum()),"regressions",int((dd<0).sum()))print("State variable names:",[k for k in list(globals()) if not k.startswith("_")][-65:])

# Hypothesis: the targeted repetition-neuron ablation has produced a causal test; recover its exact strength and paired outcomes before choosing a dose.print("Edited tensors:", edited())for name in edited():    w=load(name); w0=load_original(name)    d=w-w0    cols=torch.nonzero(d.abs().sum(0)>0).flatten() if w.ndim==2 else torch.nonzero(d!=0).flatten()    print(name, "changed columns:",cols.tolist(),          "relative norm change:",round(float(d.norm()/w0.norm()),6))    if w.ndim==2:        print("column multipliers:",[(int(j),round(float((w[:,j]*w0[:,j]).sum()/w0[:,j].square().sum()),4)) for j in cols[:40]])for fn in ["gen_4_original.jsonl","gen_4_edited.jsonl"]:    p=Path("/work")/fn    if p.exists():        rows=[json.loads(s) for s in open(p)]        flat=[dict(id=r["id"],source=r["source"],**s) for r in rows for s in r["samples"]]        print(fn,"n",len(flat),"accuracy",np.mean([r["correct"] for r in flat]),              "cut",np.mean([r["cut"] for r in flat]),"mean tokens",np.mean([r["tokens"] for r in flat]))        print("by source",[(src,sum(r["correct"] for r in flat if r["source"]==src),sum(r["source"]==src for r in flat)) for src in ["gsm8k","math"]])        globals()[fn.split(".")[0]]=flatif "gen_4_original" in globals() and "gen_4_edited" in globals():    dd=np.array([int(e["correct"])-int(o["correct"]) for o,e in zip(gen_4_original,gen_4_edited)])    print("Paired gain",float(dd.mean()),"SE",float(dd.std(ddof=1)/np.sqrt(len(dd))),"rescues",int((dd>0).sum()),"regressions",int((dd<0).sum()))print("State variable names:",[k for k in list(globals()) if not k.startswith("_")][-65:])

OUTPUT of Python run 15 of at most 300:

Edited tensors: ['model.language_model.layers.18.mlp.down_proj.weight', 'model.language_model.layers.19.mlp.down_proj.weight', 'model.language_model.layers.23.mlp.down_proj.weight']model.language_model.layers.18.mlp.down_proj.weight changed columns: [8] relative norm change: 0.017899column multipliers: [(8, 0.0)]model.language_model.layers.19.mlp.down_proj.weight changed columns: [11] relative norm change: 0.015993column multipliers: [(11, 0.0)]model.language_model.layers.23.mlp.down_proj.weight changed columns: [22, 40] relative norm change: 0.019595column multipliers: [(22, 0.0), (40, 0.0)]gen_4_original.jsonl n 128 accuracy 0.1015625 cut 0.90625 mean tokens 30206.75by source [('gsm8k', 8, 64), ('math', 5, 64)]gen_4_edited.jsonl n 128 accuracy 0.0859375 cut 0.9140625 mean tokens 30362.765625by source [('gsm8k', 7, 64), ('math', 4, 64)]Paired gain -0.015625 SE 0.02713423316130324 rescues 5 regressions 7State variable names: ['load', 'FROZEN', 'save_edit', 'drop_edit', 'edited', 'tok', 'math', 'collections', 'Path', 'gen1', 'flat1', 'idx1', 'M1', 'y1', 'mean_bad', 'mean_good', 'ids_of', 'candidates', 'E', 'nf', 'safe_words', 'safe_ids', 'safe_labels', 'down_cache', 'l', 'js', 'W', 'j', 'direction', 'scores', 'order', 'notes', 'targets', 'apply_circuit_strength', 'diag_ids', 'practice', 'file', 'by_id', 'name', 'a', 'b', 'changed', 'notes_path', 'w', 'w0', 'd', 'cols', 'p', 'ix', 'read_generations', 'g4o', 'g4e', 'label', 'rs', 'grouped', 'r', 'v', 'di', 'i', 'fn', 'rows', 'flat', 'gen_4_original', 'gen_4_edited', 'dd']

Edited tensors: ['model.language_model.layers.18.mlp.down_proj.weight', 'model.language_model.layers.19.mlp.down_proj.weight', 'model.language_model.layers.23.mlp.down_proj.weight']model.language_model.layers.18.mlp.down_proj.weight changed columns: [8] relative norm change: 0.017899column multipliers: [(8, 0.0)]model.language_model.layers.19.mlp.down_proj.weight changed columns: [11] relative norm change: 0.015993column multipliers: [(11, 0.0)]model.language_model.layers.23.mlp.down_proj.weight changed columns: [22, 40] relative norm change: 0.019595column multipliers: [(22, 0.0), (40, 0.0)]gen_4_original.jsonl n 128 accuracy 0.1015625 cut 0.90625 mean tokens 30206.75by source [('gsm8k', 8, 64), ('math', 5, 64)]gen_4_edited.jsonl n 128 accuracy 0.0859375 cut 0.9140625 mean tokens 30362.765625by source [('gsm8k', 7, 64), ('math', 4, 64)]Paired gain -0.015625 SE 0.02713423316130324 rescues 5 regressions 7State variable names: ['load', 'FROZEN', 'save_edit', 'drop_edit', 'edited', 'tok', 'math', 'collections', 'Path', 'gen1', 'flat1', 'idx1', 'M1', 'y1', 'mean_bad', 'mean_good', 'ids_of', 'candidates', 'E', 'nf', 'safe_words', 'safe_ids', 'safe_labels', 'down_cache', 'l', 'js', 'W', 'j', 'direction', 'scores', 'order', 'notes', 'targets', 'apply_circuit_strength', 'diag_ids', 'practice', 'file', 'by_id', 'name', 'a', 'b', 'changed', 'notes_path', 'w', 'w0', 'd', 'cols', 'p', 'ix', 'read_generations', 'g4o', 'g4e', 'label', 'rs', 'grouped', 'r', 'v', 'di', 'i', 'fn', 'rows', 'flat', 'gen_4_original', 'gen_4_edited', 'dd']

python

# Hypothesis: the four repetition-correlated neurons are downstream markers rather than the causal feedback source; recover the experiment record before choosing a stronger localization test.import os, json, math, collectionsfrom pathlib import Pathimport numpy as npimport torchprint(Path("/work/notes.md").read_text() if Path("/work/notes.md").exists() else "No notes file.")print("Work outputs:", sorted(os.listdir("/work")))print("Circuit targets:", targets if "targets" in globals() else "not retained")print("Candidate summary:", str(candidates)[:1200] if "candidates" in globals() else "not retained")print("Current edits:", edited())print("Model config:", Path("/model/config.json").read_text()[:5000])

# Hypothesis: the four repetition-correlated neurons are downstream markers rather than the causal feedback source; recover the experiment record before choosing a stronger localization test.import os, json, math, collectionsfrom pathlib import Pathimport numpy as npimport torchprint(Path("/work/notes.md").read_text() if Path("/work/notes.md").exists() else "No notes file.")print("Work outputs:", sorted(os.listdir("/work")))print("Circuit targets:", targets if "targets" in globals() else "not retained")print("Candidate summary:", str(candidates)[:1200] if "candidates" in globals() else "not retained")print("Current edits:", edited())print("Model config:", Path("/model/config.json").read_text()[:5000])

OUTPUT of Python run 16 of at most 300:

# Math weight-edit experimentBaseline pass 1: 256 original generations (96 GSM8K,160 MATH), full 32768-token limit.Accuracy 24/256=9.375%; 232 cutoffs (91.41%). Correct GSM=12/96, MATH=12/160.Failures enter exact repetitive thinking; 213/232 failed tails contain 'Wait,'.Repeated 12-token fractions first2048/middle/tail: failures .070/.506/.975, correct .039/.098/.177.Pass 2 (files fwd_3 after one uncounted attention-instrumentation error): two teacher-forced arithmetic/trigonometric probes, matched identical sentences at first vs repeated occurrences.Most consistent within-content repetition-associated MLP neurons:23:22 (+3.26),40 (-4.19),46 (+1.80),94 (+2.18),19 (+1.97);19:11 (-1.57);18:8 (-1.51);22:59 (+1.13).These signs agree across five distinct repetitive phrases and with failed-vs-correct generation averages.Caveat: teacher-forced chat template inserted empty thinking before supplied think; use raw templated text for follow-up.No edits tested yet. Eight forward passes remain. Need causal dose tests and independent paired validation.
Work outputs: ['edited', 'fwd_3', 'gen_1.jsonl', 'gen_1_acts', 'gen_4_edited.jsonl', 'gen_4_edited_acts', 'gen_4_original.jsonl', 'gen_4_original_acts', 'notes.md']Circuit targets: {18: [8], 19: [11], 23: [22, 40]}Candidate summary: {18: [8], 19: [11], 22: [59, 11, 89], 23: [22, 40, 46, 94, 19, 350]}Current edits: ['model.language_model.layers.18.mlp.down_proj.weight', 'model.language_model.layers.19.mlp.down_proj.weight', 'model.language_model.layers.23.mlp.down_proj.weight']Model config: {  "architectures": [    "Qwen3_5ForCausalLM"  ],  "attention_bias": false,  "attention_dropout": 0.0,  "attn_output_gate": true,  "bos_token_id": null,  "dtype": "bfloat16",  "eos_token_id": 248044,  "full_attention_interval": 4,  "head_dim": 256,  "hidden_act": "silu",  "hidden_size": 1024,  "initializer_range": 0.02,  "intermediate_size": 3584,  "layer_types": [    "linear_attention",    "linear_attention",    "full_attention",    "full_attention",    "full_attention"  ],  "linear_conv_kernel_dim": 4,  "linear_key_head_dim": 128,  "linear_num_key_heads": 16,  "linear_num_value_heads": 16,  "linear_value_head_dim": 128,  "mamba_ssm_dtype": "float32",  "max_position_embeddings": 262144,  "mlp_only_layers": [],  "model_type": "qwen3_5_text",  "mtp_num_hidden_layers": 1,  "mtp_use_dedicated_embeddings": false,  "num_attention_heads": 8,  "num_hidden_layers": 24,  "num_key_value_heads": 2,  "pad_token_id": null,  "partial_rotary_factor": 0.25,  "rms_norm_eps": 1e-06,  "rope_parameters": {    "mrope_interleaved": true,    "mrope_section": [      11,      11,      10    ],    "partial_rotary_factor": 0.25,    "rope_theta": 10000000,    "rope_type": "default"  },  "tie_word_embeddings": true,  "transformers_version": "5.17.0",  "use_cache": true,  "vocab_size": 248320}

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# Math weight-edit experimentBaseline pass 1: 256 original generations (96 GSM8K,160 MATH), full 32768-token limit.Accuracy 24/256=9.375%; 232 cutoffs (91.41%). Correct GSM=12/96, MATH=12/160.Failures enter exact repetitive thinking; 213/232 failed tails contain 'Wait,'.Repeated 12-token fractions first2048/middle/tail: failures .070/.506/.975, correct .039/.098/.177.Pass 2 (files fwd_3 after one uncounted attention-instrumentation error): two teacher-forced arithmetic/trigonometric probes, matched identical sentences at first vs repeated occurrences.Most consistent within-content repetition-associated MLP neurons:23:22 (+3.26),40 (-4.19),46 (+1.80),94 (+2.18),19 (+1.97);19:11 (-1.57);18:8 (-1.51);22:59 (+1.13).These signs agree across five distinct repetitive phrases and with failed-vs-correct generation averages.Caveat: teacher-forced chat template inserted empty thinking before supplied think; use raw templated text for follow-up.No edits tested yet. Eight forward passes remain. Need causal dose tests and independent paired validation.
Work outputs: ['edited', 'fwd_3', 'gen_1.jsonl', 'gen_1_acts', 'gen_4_edited.jsonl', 'gen_4_edited_acts', 'gen_4_original.jsonl', 'gen_4_original_acts', 'notes.md']Circuit targets: {18: [8], 19: [11], 23: [22, 40]}Candidate summary: {18: [8], 19: [11], 22: [59, 11, 89], 23: [22, 40, 46, 94, 19, 350]}Current edits: ['model.language_model.layers.18.mlp.down_proj.weight', 'model.language_model.layers.19.mlp.down_proj.weight', 'model.language_model.layers.23.mlp.down_proj.weight']Model config: {  "architectures": [    "Qwen3_5ForCausalLM"  ],  "attention_bias": false,  "attention_dropout": 0.0,  "attn_output_gate": true,  "bos_token_id": null,  "dtype": "bfloat16",  "eos_token_id": 248044,  "full_attention_interval": 4,  "head_dim": 256,  "hidden_act": "silu",  "hidden_size": 1024,  "initializer_range": 0.02,  "intermediate_size": 3584,  "layer_types": [    "linear_attention",    "linear_attention",    "full_attention",    "full_attention",    "full_attention"  ],  "linear_conv_kernel_dim": 4,  "linear_key_head_dim": 128,  "linear_num_key_heads": 16,  "linear_num_value_heads": 16,  "linear_value_head_dim": 128,  "mamba_ssm_dtype": "float32",  "max_position_embeddings": 262144,  "mlp_only_layers": [],  "model_type": "qwen3_5_text",  "mtp_num_hidden_layers": 1,  "mtp_use_dedicated_embeddings": false,  "num_attention_heads": 8,  "num_hidden_layers": 24,  "num_key_value_heads": 2,  "pad_token_id": null,  "partial_rotary_factor": 0.25,  "rms_norm_eps": 1e-06,  "rope_parameters": {    "mrope_interleaved": true,    "mrope_section": [      11,      11,      10    ],    "partial_rotary_factor": 0.25,    "rope_theta": 10000000,    "rope_type": "default"  },  "tie_word_embeddings": true,  "transformers_version": "5.17.0",  "use_cache": true,  "vocab_size": 248320}

[harness: 19 repeated lines not shown (a line is shown at most 2 times per output)]

python

# Hypothesis: the existing four-neuron intervention is causal only if it reduces repeated thinking and improves paired accuracy; reconstruct its measured result before further edits.print(Path("/work/notes.md").read_text())print("Current edits:", edited())for name in edited():    w, w0 = load(name), load_original(name)    changed = (w != w0).any(dim=0) if w.ndim==2 else w != w0    inds = torch.where(changed)[0]    print(name, "changed columns:", inds.tolist()[:30])    if w.ndim==2:        print("column scale ratios:", [(int(j),round(float((w[:,j]*w0[:,j]).sum()/w0[:,j].square().sum()),4)) for j in inds[:30]])def flatten_file(path):    return [dict(id=r["id"],source=r["source"],answer=r["answer"],**s)            for r in [json.loads(line) for line in open(path)] for s in r["samples"]]if Path("/work/gen_4_original.jsonl").exists():    b4=flatten_file("/work/gen_4_original.jsonl")    e4=flatten_file("/work/gen_4_edited.jsonl")    d4=np.array([e["correct"]-b["correct"] for b,e in zip(b4,e4)],float)    print("Pass gen_4:",len(d4),"original accuracy",np.mean([r["correct"] for r in b4]),          "edited accuracy",np.mean([r["correct"] for r in e4]),          "paired difference",d4.mean(),"SE",d4.std(ddof=1)/np.sqrt(len(d4)),          "rescued",sum(d4==1),"lost",sum(d4==-1))    for label,rows in [("original",b4),("edited",e4)]:        print(label,"cut",np.mean([r["cut"] for r in rows]),"mean tokens",np.mean([r["tokens"] for r in rows]),              "tail repetition",np.mean([repeated_fraction(ids_of(r["text"])[-4096:]) for r in rows]))        print("By source", {s:np.mean([r["correct"] for r in rows if r["source"]==s]) for s in ["gsm8k","math"]})print("Available run outputs:", sorted(p.name for p in Path("/work").glob("gen*")))

# Hypothesis: the existing four-neuron intervention is causal only if it reduces repeated thinking and improves paired accuracy; reconstruct its measured result before further edits.print(Path("/work/notes.md").read_text())print("Current edits:", edited())for name in edited():    w, w0 = load(name), load_original(name)    changed = (w != w0).any(dim=0) if w.ndim==2 else w != w0    inds = torch.where(changed)[0]    print(name, "changed columns:", inds.tolist()[:30])    if w.ndim==2:        print("column scale ratios:", [(int(j),round(float((w[:,j]*w0[:,j]).sum()/w0[:,j].square().sum()),4)) for j in inds[:30]])def flatten_file(path):    return [dict(id=r["id"],source=r["source"],answer=r["answer"],**s)            for r in [json.loads(line) for line in open(path)] for s in r["samples"]]if Path("/work/gen_4_original.jsonl").exists():    b4=flatten_file("/work/gen_4_original.jsonl")    e4=flatten_file("/work/gen_4_edited.jsonl")    d4=np.array([e["correct"]-b["correct"] for b,e in zip(b4,e4)],float)    print("Pass gen_4:",len(d4),"original accuracy",np.mean([r["correct"] for r in b4]),          "edited accuracy",np.mean([r["correct"] for r in e4]),          "paired difference",d4.mean(),"SE",d4.std(ddof=1)/np.sqrt(len(d4)),          "rescued",sum(d4==1),"lost",sum(d4==-1))    for label,rows in [("original",b4),("edited",e4)]:        print(label,"cut",np.mean([r["cut"] for r in rows]),"mean tokens",np.mean([r["tokens"] for r in rows]),              "tail repetition",np.mean([repeated_fraction(ids_of(r["text"])[-4096:]) for r in rows]))        print("By source", {s:np.mean([r["correct"] for r in rows if r["source"]==s]) for s in ["gsm8k","math"]})print("Available run outputs:", sorted(p.name for p in Path("/work").glob("gen*")))

OUTPUT of Python run 17 of at most 300:

# Math weight-edit experimentBaseline pass 1: 256 original generations (96 GSM8K,160 MATH), full 32768-token limit.Accuracy 24/256=9.375%; 232 cutoffs (91.41%). Correct GSM=12/96, MATH=12/160.Failures enter exact repetitive thinking; 213/232 failed tails contain 'Wait,'.Repeated 12-token fractions first2048/middle/tail: failures .070/.506/.975, correct .039/.098/.177.Pass 2 (files fwd_3 after one uncounted attention-instrumentation error): two teacher-forced arithmetic/trigonometric probes, matched identical sentences at first vs repeated occurrences.Most consistent within-content repetition-associated MLP neurons:23:22 (+3.26),40 (-4.19),46 (+1.80),94 (+2.18),19 (+1.97);19:11 (-1.57);18:8 (-1.51);22:59 (+1.13).These signs agree across five distinct repetitive phrases and with failed-vs-correct generation averages.Caveat: teacher-forced chat template inserted empty thinking before supplied think; use raw templated text for follow-up.No edits tested yet. Eight forward passes remain. Need causal dose tests and independent paired validation.
Current edits: ['model.language_model.layers.18.mlp.down_proj.weight', 'model.language_model.layers.19.mlp.down_proj.weight', 'model.language_model.layers.23.mlp.down_proj.weight']model.language_model.layers.18.mlp.down_proj.weight changed columns: [8]column scale ratios: [(8, 0.0)]model.language_model.layers.19.mlp.down_proj.weight changed columns: [11]column scale ratios: [(11, 0.0)]model.language_model.layers.23.mlp.down_proj.weight changed columns: [22, 40]column scale ratios: [(22, 0.0), (40, 0.0)]Pass gen_4: 128 original accuracy 0.1015625 edited accuracy 0.0859375 paired difference -0.015625 SE 0.02713423316130324 rescued 5 lost 7Traceback (most recent call last):  File "<string>", line 17, in <module>  File "<run>", line 24, in <module>  File "<run>", line 24, in <listcomp>NameError: name 'repeated_fraction' is not defined

# Math weight-edit experimentBaseline pass 1: 256 original generations (96 GSM8K,160 MATH), full 32768-token limit.Accuracy 24/256=9.375%; 232 cutoffs (91.41%). Correct GSM=12/96, MATH=12/160.Failures enter exact repetitive thinking; 213/232 failed tails contain 'Wait,'.Repeated 12-token fractions first2048/middle/tail: failures .070/.506/.975, correct .039/.098/.177.Pass 2 (files fwd_3 after one uncounted attention-instrumentation error): two teacher-forced arithmetic/trigonometric probes, matched identical sentences at first vs repeated occurrences.Most consistent within-content repetition-associated MLP neurons:23:22 (+3.26),40 (-4.19),46 (+1.80),94 (+2.18),19 (+1.97);19:11 (-1.57);18:8 (-1.51);22:59 (+1.13).These signs agree across five distinct repetitive phrases and with failed-vs-correct generation averages.Caveat: teacher-forced chat template inserted empty thinking before supplied think; use raw templated text for follow-up.No edits tested yet. Eight forward passes remain. Need causal dose tests and independent paired validation.
Current edits: ['model.language_model.layers.18.mlp.down_proj.weight', 'model.language_model.layers.19.mlp.down_proj.weight', 'model.language_model.layers.23.mlp.down_proj.weight']model.language_model.layers.18.mlp.down_proj.weight changed columns: [8]column scale ratios: [(8, 0.0)]model.language_model.layers.19.mlp.down_proj.weight changed columns: [11]column scale ratios: [(11, 0.0)]model.language_model.layers.23.mlp.down_proj.weight changed columns: [22, 40]column scale ratios: [(22, 0.0), (40, 0.0)]Pass gen_4: 128 original accuracy 0.1015625 edited accuracy 0.0859375 paired difference -0.015625 SE 0.02713423316130324 rescued 5 lost 7Traceback (most recent call last):  File "<string>", line 17, in <module>  File "<run>", line 24, in <module>  File "<run>", line 24, in <listcomp>NameError: name 'repeated_fraction' is not defined

python

# Hypothesis: the first ablation removed repetition-correlated detectors rather than its causal driver; inspect its behavioral effect before choosing another intervention.import os, json, math, collectionsfrom pathlib import Pathimport numpy as npimport torchtorch.set_num_threads(8)print("Work entries:",sorted(os.listdir("/work")))print("Notes:\n",Path("/work/notes.md").read_text() if Path("/work/notes.md").exists() else "none")print("Available diagnostic variables:", [k for k in ["flat1","idx1","M1","TFM","spans3","repeat_deltas","down_cache","by_id","diag_ids"] if k in globals()])def ids_of(s):    t=tok.encode(s)    return list(t.ids) if hasattr(t,"ids") else list(t)def repeated_fraction(ids,n=12):    if len(ids)<n: return 0.    grams=[tuple(ids[i:i+n]) for i in range(len(ids)-n+1)]    return 1-len(set(grams))/len(grams)def read_generations(path):    return [dict(id=r["id"],source=r.get("source"),answer=r.get("answer"),**s)            for line in open(path) for r in [json.loads(line)] for s in r["samples"]]for path in sorted(Path("/work").glob("gen_*.jsonl")):    data=read_generations(path)    print(path.name,"n",len(data),"acc",np.mean([r["correct"] for r in data]),          "cut",np.mean([r["cut"] for r in data]),"mean length",round(np.mean([r["tokens"] for r in data])),          "tail-repeat",round(np.mean([repeated_fraction(ids_of(r["text"][-12000:])[-2048:]) for r in data]),3))print("Current edits:",edited())

# Hypothesis: the first ablation removed repetition-correlated detectors rather than its causal driver; inspect its behavioral effect before choosing another intervention.import os, json, math, collectionsfrom pathlib import Pathimport numpy as npimport torchtorch.set_num_threads(8)print("Work entries:",sorted(os.listdir("/work")))print("Notes:\n",Path("/work/notes.md").read_text() if Path("/work/notes.md").exists() else "none")print("Available diagnostic variables:", [k for k in ["flat1","idx1","M1","TFM","spans3","repeat_deltas","down_cache","by_id","diag_ids"] if k in globals()])def ids_of(s):    t=tok.encode(s)    return list(t.ids) if hasattr(t,"ids") else list(t)def repeated_fraction(ids,n=12):    if len(ids)<n: return 0.    grams=[tuple(ids[i:i+n]) for i in range(len(ids)-n+1)]    return 1-len(set(grams))/len(grams)def read_generations(path):    return [dict(id=r["id"],source=r.get("source"),answer=r.get("answer"),**s)            for line in open(path) for r in [json.loads(line)] for s in r["samples"]]for path in sorted(Path("/work").glob("gen_*.jsonl")):    data=read_generations(path)    print(path.name,"n",len(data),"acc",np.mean([r["correct"] for r in data]),          "cut",np.mean([r["cut"] for r in data]),"mean length",round(np.mean([r["tokens"] for r in data])),          "tail-repeat",round(np.mean([repeated_fraction(ids_of(r["text"][-12000:])[-2048:]) for r in data]),3))print("Current edits:",edited())

OUTPUT of Python run 18 of at most 300:

Work entries: ['edited', 'fwd_3', 'gen_1.jsonl', 'gen_1_acts', 'gen_4_edited.jsonl', 'gen_4_edited_acts', 'gen_4_original.jsonl', 'gen_4_original_acts', 'notes.md']Notes: # Math weight-edit experimentBaseline pass 1: 256 original generations (96 GSM8K,160 MATH), full 32768-token limit.Accuracy 24/256=9.375%; 232 cutoffs (91.41%). Correct GSM=12/96, MATH=12/160.Failures enter exact repetitive thinking; 213/232 failed tails contain 'Wait,'.Repeated 12-token fractions first2048/middle/tail: failures .070/.506/.975, correct .039/.098/.177.Pass 2 (files fwd_3 after one uncounted attention-instrumentation error): two teacher-forced arithmetic/trigonometric probes, matched identical sentences at first vs repeated occurrences.Most consistent within-content repetition-associated MLP neurons:23:22 (+3.26),40 (-4.19),46 (+1.80),94 (+2.18),19 (+1.97);19:11 (-1.57);18:8 (-1.51);22:59 (+1.13).These signs agree across five distinct repetitive phrases and with failed-vs-correct generation averages.Caveat: teacher-forced chat template inserted empty thinking before supplied think; use raw templated text for follow-up.No edits tested yet. Eight forward passes remain. Need causal dose tests and independent paired validation.
Available diagnostic variables: ['flat1', 'idx1', 'M1', 'down_cache', 'by_id', 'diag_ids']gen_1.jsonl n 256 acc 0.09375 cut 0.9140625 mean length 30428 tail-repeat 0.885gen_4_edited.jsonl n 128 acc 0.0859375 cut 0.9140625 mean length 30363 tail-repeat 0.87gen_4_original.jsonl n 128 acc 0.1015625 cut 0.90625 mean length 30207 tail-repeat 0.858Current edits: ['model.language_model.layers.18.mlp.down_proj.weight', 'model.language_model.layers.19.mlp.down_proj.weight', 'model.language_model.layers.23.mlp.down_proj.weight']

Work entries: ['edited', 'fwd_3', 'gen_1.jsonl', 'gen_1_acts', 'gen_4_edited.jsonl', 'gen_4_edited_acts', 'gen_4_original.jsonl', 'gen_4_original_acts', 'notes.md']Notes: # Math weight-edit experimentBaseline pass 1: 256 original generations (96 GSM8K,160 MATH), full 32768-token limit.Accuracy 24/256=9.375%; 232 cutoffs (91.41%). Correct GSM=12/96, MATH=12/160.Failures enter exact repetitive thinking; 213/232 failed tails contain 'Wait,'.Repeated 12-token fractions first2048/middle/tail: failures .070/.506/.975, correct .039/.098/.177.Pass 2 (files fwd_3 after one uncounted attention-instrumentation error): two teacher-forced arithmetic/trigonometric probes, matched identical sentences at first vs repeated occurrences.Most consistent within-content repetition-associated MLP neurons:23:22 (+3.26),40 (-4.19),46 (+1.80),94 (+2.18),19 (+1.97);19:11 (-1.57);18:8 (-1.51);22:59 (+1.13).These signs agree across five distinct repetitive phrases and with failed-vs-correct generation averages.Caveat: teacher-forced chat template inserted empty thinking before supplied think; use raw templated text for follow-up.No edits tested yet. Eight forward passes remain. Need causal dose tests and independent paired validation.
Available diagnostic variables: ['flat1', 'idx1', 'M1', 'down_cache', 'by_id', 'diag_ids']gen_1.jsonl n 256 acc 0.09375 cut 0.9140625 mean length 30428 tail-repeat 0.885gen_4_edited.jsonl n 128 acc 0.0859375 cut 0.9140625 mean length 30363 tail-repeat 0.87gen_4_original.jsonl n 128 acc 0.1015625 cut 0.90625 mean length 30207 tail-repeat 0.858Current edits: ['model.language_model.layers.18.mlp.down_proj.weight', 'model.language_model.layers.19.mlp.down_proj.weight', 'model.language_model.layers.23.mlp.down_proj.weight']

python

# Hypothesis: the first neuron-ablation trial did not repair repetition; recover its exact intervention and paired transitions before choosing a stronger causal test.print("Relevant state names:", sorted(k for k in globals() if any(t in k.lower() for t in ["edit","trial","strength","candidate","select","dose","pass","target","head"])))for name in edited():    w0=load_original(name)    w=load(name)    changed=(w!=w0).any(dim=0)    js=torch.where(changed)[0]    factors=((w[:,js]*w0[:,js]).sum(0)/(w0[:,js].square().sum(0)+1e-20))    print(name,"columns",js.tolist(),"factors",np.round(factors.numpy(),3).tolist(),          "relative change",float((w-w0).norm()/w0.norm()))def read_flat(path):    return [dict(id=r["id"],source=r["source"],answer=r["answer"],**s)            for r in map(json.loads,open(path)) for s in r["samples"]]g4o=read_flat("/work/gen_4_original.jsonl")g4e=read_flat("/work/gen_4_edited.jsonl")print("Paired transitions:",collections.Counter((a["correct"],b["correct"]) for a,b in zip(g4o,g4e)))print("Files:",sorted(os.listdir("/work")))print("Activation files:",os.listdir("/work/gen_4_edited_acts"))print("Sample trial IDs:",[r["id"] for r in g4o[:5]])

# Hypothesis: the first neuron-ablation trial did not repair repetition; recover its exact intervention and paired transitions before choosing a stronger causal test.print("Relevant state names:", sorted(k for k in globals() if any(t in k.lower() for t in ["edit","trial","strength","candidate","select","dose","pass","target","head"])))for name in edited():    w0=load_original(name)    w=load(name)    changed=(w!=w0).any(dim=0)    js=torch.where(changed)[0]    factors=((w[:,js]*w0[:,js]).sum(0)/(w0[:,js].square().sum(0)+1e-20))    print(name,"columns",js.tolist(),"factors",np.round(factors.numpy(),3).tolist(),          "relative change",float((w-w0).norm()/w0.norm()))def read_flat(path):    return [dict(id=r["id"],source=r["source"],answer=r["answer"],**s)            for r in map(json.loads,open(path)) for s in r["samples"]]g4o=read_flat("/work/gen_4_original.jsonl")g4e=read_flat("/work/gen_4_edited.jsonl")print("Paired transitions:",collections.Counter((a["correct"],b["correct"]) for a,b in zip(g4o,g4e)))print("Files:",sorted(os.listdir("/work")))print("Activation files:",os.listdir("/work/gen_4_edited_acts"))print("Sample trial IDs:",[r["id"] for r in g4o[:5]])

OUTPUT of Python run 19 of at most 300:

Relevant state names: ['apply_circuit_strength', 'candidates', 'drop_edit', 'edited', 'gen_4_edited', 'save_edit', 'targets']model.language_model.layers.18.mlp.down_proj.weight columns [8] factors [0.0] relative change 0.017899204045534134model.language_model.layers.19.mlp.down_proj.weight columns [11] factors [0.0] relative change 0.015992557629942894model.language_model.layers.23.mlp.down_proj.weight columns [22, 40] factors [0.0, 0.0] relative change 0.019595080986618996Paired transitions: Counter({(False, False): 110, (True, False): 7, (True, True): 6, (False, True): 5})Files: ['edited', 'fwd_3', 'gen_1.jsonl', 'gen_1_acts', 'gen_4_edited.jsonl', 'gen_4_edited_acts', 'gen_4_original.jsonl', 'gen_4_original_acts', 'notes.md']Activation files: ['mlp_last_prompt.npy', 'mlp_mean_answer.npy', 'hidden_mean_answer.npy', 'index.json', 'hidden_last_prompt.npy']Sample trial IDs: ['gsm8k_train_7354', 'gsm8k_train_7354', 'gsm8k_train_4960', 'gsm8k_train_4960', 'gsm8k_train_4335']

Relevant state names: ['apply_circuit_strength', 'candidates', 'drop_edit', 'edited', 'gen_4_edited', 'save_edit', 'targets']model.language_model.layers.18.mlp.down_proj.weight columns [8] factors [0.0] relative change 0.017899204045534134model.language_model.layers.19.mlp.down_proj.weight columns [11] factors [0.0] relative change 0.015992557629942894model.language_model.layers.23.mlp.down_proj.weight columns [22, 40] factors [0.0, 0.0] relative change 0.019595080986618996Paired transitions: Counter({(False, False): 110, (True, False): 7, (True, True): 6, (False, True): 5})Files: ['edited', 'fwd_3', 'gen_1.jsonl', 'gen_1_acts', 'gen_4_edited.jsonl', 'gen_4_edited_acts', 'gen_4_original.jsonl', 'gen_4_original_acts', 'notes.md']Activation files: ['mlp_last_prompt.npy', 'mlp_mean_answer.npy', 'hidden_mean_answer.npy', 'index.json', 'hidden_last_prompt.npy']Sample trial IDs: ['gsm8k_train_7354', 'gsm8k_train_7354', 'gsm8k_train_4960', 'gsm8k_train_4960', 'gsm8k_train_4335']

python

# Hypothesis: the initial four-neuron ablation was not sufficient; recover its measured mechanism and dose before choosing the next causal intervention.print(Path("/work/notes.md").read_text())print("Available analysis state:", [k for k in globals() if any(s in k.lower() for s in ["candidate","target","strength","trial","score","circuit","gen_4","repeat","direction"])])for key in ["targets","candidates","trial_ids"]:    if key in globals():        print(key, str(globals()[key])[:1800])print("Current edits:", edited())

# Hypothesis: the initial four-neuron ablation was not sufficient; recover its measured mechanism and dose before choosing the next causal intervention.print(Path("/work/notes.md").read_text())print("Available analysis state:", [k for k in globals() if any(s in k.lower() for s in ["candidate","target","strength","trial","score","circuit","gen_4","repeat","direction"])])for key in ["targets","candidates","trial_ids"]:    if key in globals():        print(key, str(globals()[key])[:1800])print("Current edits:", edited())

OUTPUT of Python run 20 of at most 300:

# Math weight-edit experimentBaseline pass 1: 256 original generations (96 GSM8K,160 MATH), full 32768-token limit.Accuracy 24/256=9.375%; 232 cutoffs (91.41%). Correct GSM=12/96, MATH=12/160.Failures enter exact repetitive thinking; 213/232 failed tails contain 'Wait,'.Repeated 12-token fractions first2048/middle/tail: failures .070/.506/.975, correct .039/.098/.177.Pass 2 (files fwd_3 after one uncounted attention-instrumentation error): two teacher-forced arithmetic/trigonometric probes, matched identical sentences at first vs repeated occurrences.Most consistent within-content repetition-associated MLP neurons:23:22 (+3.26),40 (-4.19),46 (+1.80),94 (+2.18),19 (+1.97);19:11 (-1.57);18:8 (-1.51);22:59 (+1.13).These signs agree across five distinct repetitive phrases and with failed-vs-correct generation averages.Caveat: teacher-forced chat template inserted empty thinking before supplied think; use raw templated text for follow-up.No edits tested yet. Eight forward passes remain. Need causal dose tests and independent paired validation.
Available analysis state: ['candidates', 'direction', 'scores', 'targets', 'apply_circuit_strength', 'gen_4_original', 'gen_4_edited', 'repeated_fraction']targets {18: [8], 19: [11], 23: [22, 40]}candidates {18: [8], 19: [11], 22: [59, 11, 89], 23: [22, 40, 46, 94, 19, 350]}Current edits: ['model.language_model.layers.18.mlp.down_proj.weight', 'model.language_model.layers.19.mlp.down_proj.weight', 'model.language_model.layers.23.mlp.down_proj.weight']

# Math weight-edit experimentBaseline pass 1: 256 original generations (96 GSM8K,160 MATH), full 32768-token limit.Accuracy 24/256=9.375%; 232 cutoffs (91.41%). Correct GSM=12/96, MATH=12/160.Failures enter exact repetitive thinking; 213/232 failed tails contain 'Wait,'.Repeated 12-token fractions first2048/middle/tail: failures .070/.506/.975, correct .039/.098/.177.Pass 2 (files fwd_3 after one uncounted attention-instrumentation error): two teacher-forced arithmetic/trigonometric probes, matched identical sentences at first vs repeated occurrences.Most consistent within-content repetition-associated MLP neurons:23:22 (+3.26),40 (-4.19),46 (+1.80),94 (+2.18),19 (+1.97);19:11 (-1.57);18:8 (-1.51);22:59 (+1.13).These signs agree across five distinct repetitive phrases and with failed-vs-correct generation averages.Caveat: teacher-forced chat template inserted empty thinking before supplied think; use raw templated text for follow-up.No edits tested yet. Eight forward passes remain. Need causal dose tests and independent paired validation.
Available analysis state: ['candidates', 'direction', 'scores', 'targets', 'apply_circuit_strength', 'gen_4_original', 'gen_4_edited', 'repeated_fraction']targets {18: [8], 19: [11], 23: [22, 40]}candidates {18: [8], 19: [11], 22: [59, 11, 89], 23: [22, 40, 46, 94, 19, 350]}Current edits: ['model.language_model.layers.18.mlp.down_proj.weight', 'model.language_model.layers.19.mlp.down_proj.weight', 'model.language_model.layers.23.mlp.down_proj.weight']

python

# Hypothesis: the four-neuron intervention has already received its first causal test; recover exact strengths and paired results before further experiments.import os, json, collections, inspectimport numpy as npimport torchfrom pathlib import Pathprint("Work files:", sorted(os.listdir("/work")))for key in ["targets","candidates","direction","scores"]:    value=globals().get(key)    print(key, str(value)[:900])if "apply_circuit_strength" in globals():    try:        print(inspect.getsource(apply_circuit_strength))    except Exception:        print("Function constants:", apply_circuit_strength.__code__.co_consts)for p in sorted(Path("/work").glob("gen_*.jsonl")):    rows=[json.loads(line) for line in open(p)]    ss=[s for r in rows for s in r.get("samples",[])]    print(p.name, "n",len(ss),"accuracy",np.mean([s["correct"] for s in ss]),          "cut",np.mean([s["cut"] for s in ss]),"tokens",np.mean([s["tokens"] for s in ss]))for name in edited():    w=load(name); w0=load_original(name)    cols=torch.where((w-w0).abs().sum(0)>0)[0]    print(name,"changed columns",cols.tolist(),          "ratios",[(int(j),round(float(torch.dot(w[:,j],w0[:,j])/w0[:,j].square().sum()),4)) for j in cols])

# Hypothesis: the four-neuron intervention has already received its first causal test; recover exact strengths and paired results before further experiments.import os, json, collections, inspectimport numpy as npimport torchfrom pathlib import Pathprint("Work files:", sorted(os.listdir("/work")))for key in ["targets","candidates","direction","scores"]:    value=globals().get(key)    print(key, str(value)[:900])if "apply_circuit_strength" in globals():    try:        print(inspect.getsource(apply_circuit_strength))    except Exception:        print("Function constants:", apply_circuit_strength.__code__.co_consts)for p in sorted(Path("/work").glob("gen_*.jsonl")):    rows=[json.loads(line) for line in open(p)]    ss=[s for r in rows for s in r.get("samples",[])]    print(p.name, "n",len(ss),"accuracy",np.mean([s["correct"] for s in ss]),          "cut",np.mean([s["cut"] for s in ss]),"tokens",np.mean([s["tokens"] for s in ss]))for name in edited():    w=load(name); w0=load_original(name)    cols=torch.where((w-w0).abs().sum(0)>0)[0]    print(name,"changed columns",cols.tolist(),          "ratios",[(int(j),round(float(torch.dot(w[:,j],w0[:,j])/w0[:,j].square().sum()),4)) for j in cols])

OUTPUT of Python run 21 of at most 300:

Work files: ['edited', 'fwd_3', 'gen_1.jsonl', 'gen_1_acts', 'gen_4_edited.jsonl', 'gen_4_edited_acts', 'gen_4_original.jsonl', 'gen_4_original_acts', 'notes.md']targets {18: [8], 19: [11], 23: [22, 40]}candidates {18: [8], 19: [11], 22: [59, 11, 89], 23: [22, 40, 46, 94, 19, 350]}direction tensor([ 0.0461,  0.0137,  0.0349,  ..., -0.0066, -0.0065, -0.0013])scores [ 0.0135  0.0163  0.0145  0.0149  0.0151  0.0141  0.017   0.0159  0.0128  0.0159  0.0216  0.0199  0.0208  0.017   0.0236  0.0178  0.0199  0.0157  0.0095  0.0171  0.0117  0.0199  0.014   0.016   0.0107  0.0134  0.0141  0.0092  0.0067  0.0041  0.0134  0.0034  0.0117 -0.0101 -0.0069]Function constants: (None, 'model.language_model.layers.', '.mlp.down_proj.weight', 1)gen_1.jsonl n 256 accuracy 0.09375 cut 0.9140625 tokens 30428.4765625gen_4_edited.jsonl n 128 accuracy 0.0859375 cut 0.9140625 tokens 30362.765625gen_4_original.jsonl n 128 accuracy 0.1015625 cut 0.90625 tokens 30206.75model.language_model.layers.18.mlp.down_proj.weight changed columns [8] ratios [(8, 0.0)]model.language_model.layers.19.mlp.down_proj.weight changed columns [11] ratios [(11, 0.0)]model.language_model.layers.23.mlp.down_proj.weight changed columns [22, 40] ratios [(22, 0.0), (40, 0.0)]

Work files: ['edited', 'fwd_3', 'gen_1.jsonl', 'gen_1_acts', 'gen_4_edited.jsonl', 'gen_4_edited_acts', 'gen_4_original.jsonl', 'gen_4_original_acts', 'notes.md']targets {18: [8], 19: [11], 23: [22, 40]}candidates {18: [8], 19: [11], 22: [59, 11, 89], 23: [22, 40, 46, 94, 19, 350]}direction tensor([ 0.0461,  0.0137,  0.0349,  ..., -0.0066, -0.0065, -0.0013])scores [ 0.0135  0.0163  0.0145  0.0149  0.0151  0.0141  0.017   0.0159  0.0128  0.0159  0.0216  0.0199  0.0208  0.017   0.0236  0.0178  0.0199  0.0157  0.0095  0.0171  0.0117  0.0199  0.014   0.016   0.0107  0.0134  0.0141  0.0092  0.0067  0.0041  0.0134  0.0034  0.0117 -0.0101 -0.0069]Function constants: (None, 'model.language_model.layers.', '.mlp.down_proj.weight', 1)gen_1.jsonl n 256 accuracy 0.09375 cut 0.9140625 tokens 30428.4765625gen_4_edited.jsonl n 128 accuracy 0.0859375 cut 0.9140625 tokens 30362.765625gen_4_original.jsonl n 128 accuracy 0.1015625 cut 0.90625 tokens 30206.75model.language_model.layers.18.mlp.down_proj.weight changed columns [8] ratios [(8, 0.0)]model.language_model.layers.19.mlp.down_proj.weight changed columns [11] ratios [(11, 0.0)]model.language_model.layers.23.mlp.down_proj.weight changed columns [22, 40] ratios [(22, 0.0), (40, 0.0)]

python

# Hypothesis: the initial four-neuron ablation did not causally repair repetition, so its negative result must guide a narrower or different intervention.print(Path("/work/notes.md").read_text())print("Defined analysis variables:", sorted(k for k in globals() if not k.startswith("_") and k not in ["NAMES","by_id","practice","flat1","gen1","TFM","down_cache"]))print("Current edits:", edited())

# Hypothesis: the initial four-neuron ablation did not causally repair repetition, so its negative result must guide a narrower or different intervention.print(Path("/work/notes.md").read_text())print("Defined analysis variables:", sorted(k for k in globals() if not k.startswith("_") and k not in ["NAMES","by_id","practice","flat1","gen1","TFM","down_cache"]))print("Current edits:", edited())

OUTPUT of Python run 22 of at most 300:

# Math weight-edit experimentBaseline pass 1: 256 original generations (96 GSM8K,160 MATH), full 32768-token limit.Accuracy 24/256=9.375%; 232 cutoffs (91.41%). Correct GSM=12/96, MATH=12/160.Failures enter exact repetitive thinking; 213/232 failed tails contain 'Wait,'.Repeated 12-token fractions first2048/middle/tail: failures .070/.506/.975, correct .039/.098/.177.Pass 2 (files fwd_3 after one uncounted attention-instrumentation error): two teacher-forced arithmetic/trigonometric probes, matched identical sentences at first vs repeated occurrences.Most consistent within-content repetition-associated MLP neurons:23:22 (+3.26),40 (-4.19),46 (+1.80),94 (+2.18),19 (+1.97);19:11 (-1.57);18:8 (-1.51);22:59 (+1.13).These signs agree across five distinct repetitive phrases and with failed-vs-correct generation averages.Caveat: teacher-forced chat template inserted empty thinking before supplied think; use raw templated text for follow-up.No edits tested yet. Eight forward passes remain. Need causal dose tests and independent paired validation.
Defined analysis variables: ['E', 'FROZEN', 'M1', 'MODEL', 'Path', 'Tokenizer', 'W', 'WORK', 'a', 'apply_circuit_strength', 'b', 'b4', 'candidates', 'changed', 'collections', 'cols', 'd', 'd4', 'data', 'dd', 'di', 'diag_ids', 'direction', 'drop_edit', 'e4', 'edited', 'factors', 'file', 'flat', 'flatten_file', 'fn', 'g4e', 'g4o', 'gen_4_edited', 'gen_4_original', 'grouped', 'i', 'ids_of', 'idx1', 'inds', 'inspect', 'ix', 'j', 'js', 'json', 'key', 'l', 'label', 'load', 'load_original', 'math', 'mean_bad', 'mean_good', 'name', 'nf', 'notes', 'notes_path', 'np', 'order', 'os', 'p', 'path', 'r', 'read_flat', 'read_generations', 'repeated_fraction', 'rows', 'rs', 'safe_ids', 'safe_labels', 'safe_open', 'safe_words', 'save_edit', 'scores', 'ss', 'targets', 'tok', 'torch', 'v', 'value', 'w', 'w0', 'y1']Current edits: ['model.language_model.layers.18.mlp.down_proj.weight', 'model.language_model.layers.19.mlp.down_proj.weight', 'model.language_model.layers.23.mlp.down_proj.weight']

# Math weight-edit experimentBaseline pass 1: 256 original generations (96 GSM8K,160 MATH), full 32768-token limit.Accuracy 24/256=9.375%; 232 cutoffs (91.41%). Correct GSM=12/96, MATH=12/160.Failures enter exact repetitive thinking; 213/232 failed tails contain 'Wait,'.Repeated 12-token fractions first2048/middle/tail: failures .070/.506/.975, correct .039/.098/.177.Pass 2 (files fwd_3 after one uncounted attention-instrumentation error): two teacher-forced arithmetic/trigonometric probes, matched identical sentences at first vs repeated occurrences.Most consistent within-content repetition-associated MLP neurons:23:22 (+3.26),40 (-4.19),46 (+1.80),94 (+2.18),19 (+1.97);19:11 (-1.57);18:8 (-1.51);22:59 (+1.13).These signs agree across five distinct repetitive phrases and with failed-vs-correct generation averages.Caveat: teacher-forced chat template inserted empty thinking before supplied think; use raw templated text for follow-up.No edits tested yet. Eight forward passes remain. Need causal dose tests and independent paired validation.
Defined analysis variables: ['E', 'FROZEN', 'M1', 'MODEL', 'Path', 'Tokenizer', 'W', 'WORK', 'a', 'apply_circuit_strength', 'b', 'b4', 'candidates', 'changed', 'collections', 'cols', 'd', 'd4', 'data', 'dd', 'di', 'diag_ids', 'direction', 'drop_edit', 'e4', 'edited', 'factors', 'file', 'flat', 'flatten_file', 'fn', 'g4e', 'g4o', 'gen_4_edited', 'gen_4_original', 'grouped', 'i', 'ids_of', 'idx1', 'inds', 'inspect', 'ix', 'j', 'js', 'json', 'key', 'l', 'label', 'load', 'load_original', 'math', 'mean_bad', 'mean_good', 'name', 'nf', 'notes', 'notes_path', 'np', 'order', 'os', 'p', 'path', 'r', 'read_flat', 'read_generations', 'repeated_fraction', 'rows', 'rs', 'safe_ids', 'safe_labels', 'safe_open', 'safe_words', 'save_edit', 'scores', 'ss', 'targets', 'tok', 'torch', 'v', 'value', 'w', 'w0', 'y1']Current edits: ['model.language_model.layers.18.mlp.down_proj.weight', 'model.language_model.layers.19.mlp.down_proj.weight', 'model.language_model.layers.23.mlp.down_proj.weight']

python

# Hypothesis: the repetition-circuit intervention must be assessed against its paired causal test before any further edit is chosen.print("Work files:", sorted(os.listdir("/work")))print("Current edits:", edited())for key in ["candidates", "targets", "factors"]:    if key in globals():        print(key, globals()[key])for name in edited():    w0 = load_original(name)    w = load(name)    changed_cols = torch.where((w != w0).any(dim=0))[0]    print(name, "changed columns:", changed_cols.tolist(),          "relative change:", float((w-w0).norm()/w0.norm()))    for j in changed_cols[:15]:        ratio = float(torch.dot(w[:,j],w0[:,j])/torch.dot(w0[:,j],w0[:,j]))        print(" column", int(j), "retained coefficient", round(ratio,5))for path in sorted(Path("/work").glob("gen_*.jsonl")):    data = [json.loads(s) for s in open(path)]    flat = [dict(id=r["id"], source=r.get("source"), **s) for r in data for s in r["samples"]]    print(path.name, "n",len(flat),"correct",sum(bool(r["correct"]) for r in flat),          "cuts",sum(bool(r["cut"]) for r in flat),"mean tokens",round(np.mean([r["tokens"] for r in flat])))

# Hypothesis: the repetition-circuit intervention must be assessed against its paired causal test before any further edit is chosen.print("Work files:", sorted(os.listdir("/work")))print("Current edits:", edited())for key in ["candidates", "targets", "factors"]:    if key in globals():        print(key, globals()[key])for name in edited():    w0 = load_original(name)    w = load(name)    changed_cols = torch.where((w != w0).any(dim=0))[0]    print(name, "changed columns:", changed_cols.tolist(),          "relative change:", float((w-w0).norm()/w0.norm()))    for j in changed_cols[:15]:        ratio = float(torch.dot(w[:,j],w0[:,j])/torch.dot(w0[:,j],w0[:,j]))        print(" column", int(j), "retained coefficient", round(ratio,5))for path in sorted(Path("/work").glob("gen_*.jsonl")):    data = [json.loads(s) for s in open(path)]    flat = [dict(id=r["id"], source=r.get("source"), **s) for r in data for s in r["samples"]]    print(path.name, "n",len(flat),"correct",sum(bool(r["correct"]) for r in flat),          "cuts",sum(bool(r["cut"]) for r in flat),"mean tokens",round(np.mean([r["tokens"] for r in flat])))

OUTPUT of Python run 23 of at most 300:

Work files: ['edited', 'fwd_3', 'gen_1.jsonl', 'gen_1_acts', 'gen_4_edited.jsonl', 'gen_4_edited_acts', 'gen_4_original.jsonl', 'gen_4_original_acts', 'notes.md']Current edits: ['model.language_model.layers.18.mlp.down_proj.weight', 'model.language_model.layers.19.mlp.down_proj.weight', 'model.language_model.layers.23.mlp.down_proj.weight']candidates {18: [8], 19: [11], 22: [59, 11, 89], 23: [22, 40, 46, 94, 19, 350]}targets {18: [8], 19: [11], 23: [22, 40]}factors tensor([0., 0.])model.language_model.layers.18.mlp.down_proj.weight changed columns: [8] relative change: 0.017899204045534134 column 8 retained coefficient 0.0model.language_model.layers.19.mlp.down_proj.weight changed columns: [11] relative change: 0.015992557629942894 column 11 retained coefficient 0.0model.language_model.layers.23.mlp.down_proj.weight changed columns: [22, 40] relative change: 0.019595080986618996 column 22 retained coefficient 0.0 column 40 retained coefficient 0.0gen_1.jsonl n 256 correct 24 cuts 234 mean tokens 30428gen_4_edited.jsonl n 128 correct 11 cuts 117 mean tokens 30363gen_4_original.jsonl n 128 correct 13 cuts 116 mean tokens 30207

Work files: ['edited', 'fwd_3', 'gen_1.jsonl', 'gen_1_acts', 'gen_4_edited.jsonl', 'gen_4_edited_acts', 'gen_4_original.jsonl', 'gen_4_original_acts', 'notes.md']Current edits: ['model.language_model.layers.18.mlp.down_proj.weight', 'model.language_model.layers.19.mlp.down_proj.weight', 'model.language_model.layers.23.mlp.down_proj.weight']candidates {18: [8], 19: [11], 22: [59, 11, 89], 23: [22, 40, 46, 94, 19, 350]}targets {18: [8], 19: [11], 23: [22, 40]}factors tensor([0., 0.])model.language_model.layers.18.mlp.down_proj.weight changed columns: [8] relative change: 0.017899204045534134 column 8 retained coefficient 0.0model.language_model.layers.19.mlp.down_proj.weight changed columns: [11] relative change: 0.015992557629942894 column 11 retained coefficient 0.0model.language_model.layers.23.mlp.down_proj.weight changed columns: [22, 40] relative change: 0.019595080986618996 column 22 retained coefficient 0.0 column 40 retained coefficient 0.0gen_1.jsonl n 256 correct 24 cuts 234 mean tokens 30428gen_4_edited.jsonl n 128 correct 11 cuts 117 mean tokens 30363gen_4_original.jsonl n 128 correct 13 cuts 116 mean tokens 30207

python

# Hypothesis: the four-neuron ablation did not repair the loop, so we must recover its measured effects before choosing a more causally specific intervention.print(Path("/work/notes.md").read_text())print("Forward metadata files:", sorted(str(p) for p in Path("/work").glob("fwd_*/meta.json")))print("Available analysis variables:", sorted(k for k in globals() if not k.startswith("_") and any(s in k.lower() for s in ["target","candidate","score","delta","stat","head","gen4","span","probe","edit","dose"])))for p in ["/work/gen_4_original_acts/index.json","/work/gen_4_edited_acts/index.json"]:    if os.path.exists(p):        d=json.load(open(p))        print(p, "layers",d.get("layers"),"mlp_layers",d.get("mlp_layers"),"n",len(d.get("samples",[])))

# Hypothesis: the four-neuron ablation did not repair the loop, so we must recover its measured effects before choosing a more causally specific intervention.print(Path("/work/notes.md").read_text())print("Forward metadata files:", sorted(str(p) for p in Path("/work").glob("fwd_*/meta.json")))print("Available analysis variables:", sorted(k for k in globals() if not k.startswith("_") and any(s in k.lower() for s in ["target","candidate","score","delta","stat","head","gen4","span","probe","edit","dose"])))for p in ["/work/gen_4_original_acts/index.json","/work/gen_4_edited_acts/index.json"]:    if os.path.exists(p):        d=json.load(open(p))        print(p, "layers",d.get("layers"),"mlp_layers",d.get("mlp_layers"),"n",len(d.get("samples",[])))

OUTPUT of Python run 24 of at most 300:

# Math weight-edit experimentBaseline pass 1: 256 original generations (96 GSM8K,160 MATH), full 32768-token limit.Accuracy 24/256=9.375%; 232 cutoffs (91.41%). Correct GSM=12/96, MATH=12/160.Failures enter exact repetitive thinking; 213/232 failed tails contain 'Wait,'.Repeated 12-token fractions first2048/middle/tail: failures .070/.506/.975, correct .039/.098/.177.Pass 2 (files fwd_3 after one uncounted attention-instrumentation error): two teacher-forced arithmetic/trigonometric probes, matched identical sentences at first vs repeated occurrences.Most consistent within-content repetition-associated MLP neurons:23:22 (+3.26),40 (-4.19),46 (+1.80),94 (+2.18),19 (+1.97);19:11 (-1.57);18:8 (-1.51);22:59 (+1.13).These signs agree across five distinct repetitive phrases and with failed-vs-correct generation averages.Caveat: teacher-forced chat template inserted empty thinking before supplied think; use raw templated text for follow-up.No edits tested yet. Eight forward passes remain. Need causal dose tests and independent paired validation.
Forward metadata files: ['/work/fwd_3/meta.json']Available analysis variables: ['candidates', 'drop_edit', 'edited', 'gen_4_edited', 'save_edit', 'scores', 'targets']/work/gen_4_original_acts/index.json layers [18, 19, 23] mlp_layers [18, 19, 23] n 128/work/gen_4_edited_acts/index.json layers [18, 19, 23] mlp_layers [18, 19, 23] n 128

# Math weight-edit experimentBaseline pass 1: 256 original generations (96 GSM8K,160 MATH), full 32768-token limit.Accuracy 24/256=9.375%; 232 cutoffs (91.41%). Correct GSM=12/96, MATH=12/160.Failures enter exact repetitive thinking; 213/232 failed tails contain 'Wait,'.Repeated 12-token fractions first2048/middle/tail: failures .070/.506/.975, correct .039/.098/.177.Pass 2 (files fwd_3 after one uncounted attention-instrumentation error): two teacher-forced arithmetic/trigonometric probes, matched identical sentences at first vs repeated occurrences.Most consistent within-content repetition-associated MLP neurons:23:22 (+3.26),40 (-4.19),46 (+1.80),94 (+2.18),19 (+1.97);19:11 (-1.57);18:8 (-1.51);22:59 (+1.13).These signs agree across five distinct repetitive phrases and with failed-vs-correct generation averages.Caveat: teacher-forced chat template inserted empty thinking before supplied think; use raw templated text for follow-up.No edits tested yet. Eight forward passes remain. Need causal dose tests and independent paired validation.
Forward metadata files: ['/work/fwd_3/meta.json']Available analysis variables: ['candidates', 'drop_edit', 'edited', 'gen_4_edited', 'save_edit', 'scores', 'targets']/work/gen_4_original_acts/index.json layers [18, 19, 23] mlp_layers [18, 19, 23] n 128/work/gen_4_edited_acts/index.json layers [18, 19, 23] mlp_layers [18, 19, 23] n 128

python

# Hypothesis: the current edit and any intervening paired results can be reconstructed from saved artifacts before choosing the next causal dose.import os, json, collections, numpy as np, torchfrom pathlib import Pathprint("Work files:", sorted(os.listdir("/work")))print("Current edits:", edited())for key in ["targets", "candidates", "scores"]:    if key in globals():        value=globals()[key]        print(key, str(value)[:1600])def read_generations(path):    rows=[json.loads(line) for line in open(path)]    return [dict(id=r["id"],source=r.get("source"),**s) for r in rows for s in r["samples"]]for path in sorted(Path("/work").glob("gen_*.jsonl")):    rows=read_generations(path)    print(path.name, "n",len(rows),"correct",sum(r["correct"] for r in rows),          "cut",sum(r["cut"] for r in rows),"mean tokens",round(np.mean([r["tokens"] for r in rows])))for name in edited():    w=load(name); o=load_original(name)    changed=(w-o).abs()    print(name,"relative norm",float(changed.norm()/o.norm()),          "changed columns",torch.where(changed.max(0).values>0)[0].tolist()[:100] if w.ndim==2 else "vector")

# Hypothesis: the current edit and any intervening paired results can be reconstructed from saved artifacts before choosing the next causal dose.import os, json, collections, numpy as np, torchfrom pathlib import Pathprint("Work files:", sorted(os.listdir("/work")))print("Current edits:", edited())for key in ["targets", "candidates", "scores"]:    if key in globals():        value=globals()[key]        print(key, str(value)[:1600])def read_generations(path):    rows=[json.loads(line) for line in open(path)]    return [dict(id=r["id"],source=r.get("source"),**s) for r in rows for s in r["samples"]]for path in sorted(Path("/work").glob("gen_*.jsonl")):    rows=read_generations(path)    print(path.name, "n",len(rows),"correct",sum(r["correct"] for r in rows),          "cut",sum(r["cut"] for r in rows),"mean tokens",round(np.mean([r["tokens"] for r in rows])))for name in edited():    w=load(name); o=load_original(name)    changed=(w-o).abs()    print(name,"relative norm",float(changed.norm()/o.norm()),          "changed columns",torch.where(changed.max(0).values>0)[0].tolist()[:100] if w.ndim==2 else "vector")

OUTPUT of Python run 25 of at most 300:

Work files: ['edited', 'fwd_3', 'gen_1.jsonl', 'gen_1_acts', 'gen_4_edited.jsonl', 'gen_4_edited_acts', 'gen_4_original.jsonl', 'gen_4_original_acts', 'notes.md']Current edits: ['model.language_model.layers.18.mlp.down_proj.weight', 'model.language_model.layers.19.mlp.down_proj.weight', 'model.language_model.layers.23.mlp.down_proj.weight']targets {18: [8], 19: [11], 23: [22, 40]}candidates {18: [8], 19: [11], 22: [59, 11, 89], 23: [22, 40, 46, 94, 19, 350]}scores [ 0.0135  0.0163  0.0145  0.0149  0.0151  0.0141  0.017   0.0159  0.0128  0.0159  0.0216  0.0199  0.0208  0.017   0.0236  0.0178  0.0199  0.0157  0.0095  0.0171  0.0117  0.0199  0.014   0.016   0.0107  0.0134  0.0141  0.0092  0.0067  0.0041  0.0134  0.0034  0.0117 -0.0101 -0.0069]gen_1.jsonl n 256 correct 24 cut 234 mean tokens 30428gen_4_edited.jsonl n 128 correct 11 cut 117 mean tokens 30363gen_4_original.jsonl n 128 correct 13 cut 116 mean tokens 30207model.language_model.layers.18.mlp.down_proj.weight relative norm 0.017899204045534134 changed columns [8]model.language_model.layers.19.mlp.down_proj.weight relative norm 0.015992557629942894 changed columns [11]model.language_model.layers.23.mlp.down_proj.weight relative norm 0.019595080986618996 changed columns [22, 40]

Work files: ['edited', 'fwd_3', 'gen_1.jsonl', 'gen_1_acts', 'gen_4_edited.jsonl', 'gen_4_edited_acts', 'gen_4_original.jsonl', 'gen_4_original_acts', 'notes.md']Current edits: ['model.language_model.layers.18.mlp.down_proj.weight', 'model.language_model.layers.19.mlp.down_proj.weight', 'model.language_model.layers.23.mlp.down_proj.weight']targets {18: [8], 19: [11], 23: [22, 40]}candidates {18: [8], 19: [11], 22: [59, 11, 89], 23: [22, 40, 46, 94, 19, 350]}scores [ 0.0135  0.0163  0.0145  0.0149  0.0151  0.0141  0.017   0.0159  0.0128  0.0159  0.0216  0.0199  0.0208  0.017   0.0236  0.0178  0.0199  0.0157  0.0095  0.0171  0.0117  0.0199  0.014   0.016   0.0107  0.0134  0.0141  0.0092  0.0067  0.0041  0.0134  0.0034  0.0117 -0.0101 -0.0069]gen_1.jsonl n 256 correct 24 cut 234 mean tokens 30428gen_4_edited.jsonl n 128 correct 11 cut 117 mean tokens 30363gen_4_original.jsonl n 128 correct 13 cut 116 mean tokens 30207model.language_model.layers.18.mlp.down_proj.weight relative norm 0.017899204045534134 changed columns [8]model.language_model.layers.19.mlp.down_proj.weight relative norm 0.015992557629942894 changed columns [11]model.language_model.layers.23.mlp.down_proj.weight relative norm 0.019595080986618996 changed columns [22, 40]

python

# Hypothesis: the initial four-neuron attenuation was not causally sufficient; recover its exact strength and paired outcomes before choosing another intervention.print(Path("/work/notes.md").read_text())print("Available result folders:", sorted(p.name for p in Path("/work").iterdir()))print("Relevant kernel variables:", [k for k in globals() if any(s in k.lower() for s in ["strength","dose","target","candidate","pass","trial"])])for name in edited():    w=load(name); o=load_original(name)    js=torch.where((w-o).square().sum(0)>0)[0]    print(name, [(int(j),float(torch.dot(w[:,j],o[:,j])/o[:,j].square().sum())) for j in js])

# Hypothesis: the initial four-neuron attenuation was not causally sufficient; recover its exact strength and paired outcomes before choosing another intervention.print(Path("/work/notes.md").read_text())print("Available result folders:", sorted(p.name for p in Path("/work").iterdir()))print("Relevant kernel variables:", [k for k in globals() if any(s in k.lower() for s in ["strength","dose","target","candidate","pass","trial"])])for name in edited():    w=load(name); o=load_original(name)    js=torch.where((w-o).square().sum(0)>0)[0]    print(name, [(int(j),float(torch.dot(w[:,j],o[:,j])/o[:,j].square().sum())) for j in js])

OUTPUT of Python run 26 of at most 300:

# Math weight-edit experimentBaseline pass 1: 256 original generations (96 GSM8K,160 MATH), full 32768-token limit.Accuracy 24/256=9.375%; 232 cutoffs (91.41%). Correct GSM=12/96, MATH=12/160.Failures enter exact repetitive thinking; 213/232 failed tails contain 'Wait,'.Repeated 12-token fractions first2048/middle/tail: failures .070/.506/.975, correct .039/.098/.177.Pass 2 (files fwd_3 after one uncounted attention-instrumentation error): two teacher-forced arithmetic/trigonometric probes, matched identical sentences at first vs repeated occurrences.Most consistent within-content repetition-associated MLP neurons:23:22 (+3.26),40 (-4.19),46 (+1.80),94 (+2.18),19 (+1.97);19:11 (-1.57);18:8 (-1.51);22:59 (+1.13).These signs agree across five distinct repetitive phrases and with failed-vs-correct generation averages.Caveat: teacher-forced chat template inserted empty thinking before supplied think; use raw templated text for follow-up.No edits tested yet. Eight forward passes remain. Need causal dose tests and independent paired validation.
Available result folders: ['edited', 'fwd_3', 'gen_1.jsonl', 'gen_1_acts', 'gen_4_edited.jsonl', 'gen_4_edited_acts', 'gen_4_original.jsonl', 'gen_4_original_acts', 'notes.md']Relevant kernel variables: ['candidates', 'targets', 'apply_circuit_strength']model.language_model.layers.18.mlp.down_proj.weight [(8, 0.0)]model.language_model.layers.19.mlp.down_proj.weight [(11, 0.0)]model.language_model.layers.23.mlp.down_proj.weight [(22, 0.0), (40, 0.0)]

# Math weight-edit experimentBaseline pass 1: 256 original generations (96 GSM8K,160 MATH), full 32768-token limit.Accuracy 24/256=9.375%; 232 cutoffs (91.41%). Correct GSM=12/96, MATH=12/160.Failures enter exact repetitive thinking; 213/232 failed tails contain 'Wait,'.Repeated 12-token fractions first2048/middle/tail: failures .070/.506/.975, correct .039/.098/.177.Pass 2 (files fwd_3 after one uncounted attention-instrumentation error): two teacher-forced arithmetic/trigonometric probes, matched identical sentences at first vs repeated occurrences.Most consistent within-content repetition-associated MLP neurons:23:22 (+3.26),40 (-4.19),46 (+1.80),94 (+2.18),19 (+1.97);19:11 (-1.57);18:8 (-1.51);22:59 (+1.13).These signs agree across five distinct repetitive phrases and with failed-vs-correct generation averages.Caveat: teacher-forced chat template inserted empty thinking before supplied think; use raw templated text for follow-up.No edits tested yet. Eight forward passes remain. Need causal dose tests and independent paired validation.
Available result folders: ['edited', 'fwd_3', 'gen_1.jsonl', 'gen_1_acts', 'gen_4_edited.jsonl', 'gen_4_edited_acts', 'gen_4_original.jsonl', 'gen_4_original_acts', 'notes.md']Relevant kernel variables: ['candidates', 'targets', 'apply_circuit_strength']model.language_model.layers.18.mlp.down_proj.weight [(8, 0.0)]model.language_model.layers.19.mlp.down_proj.weight [(11, 0.0)]model.language_model.layers.23.mlp.down_proj.weight [(22, 0.0), (40, 0.0)]

python

# Hypothesis: the four-neuron ablation should reduce repetitive thinking; recover its paired results before choosing the next dose.print("Current edits:", edited())print("Circuit targets:", targets if "targets" in globals() else "not in kernel")for filename in ["gen_4_original.jsonl", "gen_4_edited.jsonl"]:    rows = [json.loads(s) for s in open("/work/"+filename)]    flat = [dict(id=r["id"],source=r["source"],**s) for r in rows for s in r["samples"]]    globals()[filename.split(".")[0]] = flat    print(filename,"n",len(flat),"correct",sum(r["correct"] for r in flat),          "cut",sum(r["cut"] for r in flat),"mean_tokens",round(np.mean([r["tokens"] for r in flat])))    print("By source:",{src:(sum(r["correct"] for r in flat if r["source"]==src),                              sum(r["source"]==src for r in flat)) for src in ["gsm8k","math"]})orig4, edit4 = gen_4_original, gen_4_editedd=np.array([int(b["correct"])-int(a["correct"]) for a,b in zip(orig4,edit4)])print("Paired gain",round(float(d.mean()),4),"SE",round(float(d.std(ddof=1)/np.sqrt(len(d))),4),      "rescued",int((d==1).sum()),"harmed",int((d==-1).sum()))print("Circuit helper:",getattr(apply_circuit_strength,"__doc__",None))print("Causal sample IDs:",json.dumps([r["id"] for r in orig4]))

# Hypothesis: the four-neuron ablation should reduce repetitive thinking; recover its paired results before choosing the next dose.print("Current edits:", edited())print("Circuit targets:", targets if "targets" in globals() else "not in kernel")for filename in ["gen_4_original.jsonl", "gen_4_edited.jsonl"]:    rows = [json.loads(s) for s in open("/work/"+filename)]    flat = [dict(id=r["id"],source=r["source"],**s) for r in rows for s in r["samples"]]    globals()[filename.split(".")[0]] = flat    print(filename,"n",len(flat),"correct",sum(r["correct"] for r in flat),          "cut",sum(r["cut"] for r in flat),"mean_tokens",round(np.mean([r["tokens"] for r in flat])))    print("By source:",{src:(sum(r["correct"] for r in flat if r["source"]==src),                              sum(r["source"]==src for r in flat)) for src in ["gsm8k","math"]})orig4, edit4 = gen_4_original, gen_4_editedd=np.array([int(b["correct"])-int(a["correct"]) for a,b in zip(orig4,edit4)])print("Paired gain",round(float(d.mean()),4),"SE",round(float(d.std(ddof=1)/np.sqrt(len(d))),4),      "rescued",int((d==1).sum()),"harmed",int((d==-1).sum()))print("Circuit helper:",getattr(apply_circuit_strength,"__doc__",None))print("Causal sample IDs:",json.dumps([r["id"] for r in orig4]))

OUTPUT of Python run 27 of at most 300:

Current edits: ['model.language_model.layers.18.mlp.down_proj.weight', 'model.language_model.layers.19.mlp.down_proj.weight', 'model.language_model.layers.23.mlp.down_proj.weight']Circuit targets: {18: [8], 19: [11], 23: [22, 40]}gen_4_original.jsonl n 128 correct 13 cut 116 mean_tokens 30207By source: {'gsm8k': (8, 64), 'math': (5, 64)}gen_4_edited.jsonl n 128 correct 11 cut 117 mean_tokens 30363By source: {'gsm8k': (7, 64), 'math': (4, 64)}Paired gain -0.0156 SE 0.0271 rescued 5 harmed 7Circuit helper: NoneCausal sample IDs: ["gsm8k_train_7354", "gsm8k_train_7354", "gsm8k_train_4960", "gsm8k_train_4960", "gsm8k_train_4335", "gsm8k_train_4335", "gsm8k_train_3553", "gsm8k_train_3553", "gsm8k_train_5789", "gsm8k_train_5789", "gsm8k_train_1381", "gsm8k_train_1381", "gsm8k_train_1927", "gsm8k_train_1927", "gsm8k_train_5240", "gsm8k_train_5240", "gsm8k_train_2157", "gsm8k_train_2157", "gsm8k_train_494", "gsm8k_train_494", "gsm8k_train_7076", "gsm8k_train_7076", "gsm8k_train_6526", "gsm8k_train_6526", "gsm8k_train_422", "gsm8k_train_422", "gsm8k_train_975", "gsm8k_train_975", "gsm8k_train_1387", "gsm8k_train_1387", "gsm8k_train_5866", "gsm8k_train_5866", "gsm8k_train_462", "gsm8k_train_462", "gsm8k_train_2739", "gsm8k_train_2739", "gsm8k_train_0", "gsm8k_train_0", "gsm8k_train_5989", "gsm8k_train_5989", "gsm8k_train_1028", "gsm8k_train_1028", "gsm8k_train_714", "gsm8k_train_714", "gsm8k_train_7181", "gsm8k_train_7181", "gsm8k_train_350", "gsm8k_train_350", "gsm8k_train_7198", "gsm8k_train_7198", "gsm8k_train_3835", "gsm8k_train_3835", "gsm8k_train_2079", "gsm8k_train_2079", "gsm8k_train_5000", "gsm8k_train_5000", "gsm8k_train_7182", "gsm8k_train_7182", "gsm8k_train_2193", "gsm8k_train_2193", "gsm8k_train_576", "gsm8k_train_576", "gsm8k_train_1098", "gsm8k_train_1098", "math_train_precalculus_587", "math_train_precalculus_587", "math_train_geometry_350", "math_train_geometry_350", "math_train_intermediate_algebra_1136", "math_train_intermediate_algebra_1136", "math_train_algebra_603", "math_train_algebra_603", "math_train_prealgebra_68", "math_train_prealgebra_68", "math_train_intermediate_algebra_895", "math_train_intermediate_algebra_895", "math_train_intermediate_algebra_497", "math_train_intermediate_algebra_497", "math_train_counting_and_probability_685", "math_train_counting_and_probability_685", "math_train_algebra_635", "math_train_algebra_635", "math_train_geometry_67", "math_train_geometry_67", "math_train_prealgebra_321", "math_train_prealgebra_321", "math_train_prealgebra_831", "math_train_prealgebra_831", "math_train_algebra_469", "math_train_algebra_469", "math_train_counting_and_probability_483", "math_train_counting_and_probability_483", "math_train_algebra_241", "math_train_algebra_241", "math_train_counting_and_probability_527", "math_train_counting_and_probability_527", "math_train_counting_and_probability_644", "math_train_counting_and_probability_644", "math_train_algebra_209", "math_train_algebra_209", "math_train_intermediate_algebra_732", "math_train_intermediate_algebra_732", "math_train_number_theory_174", "math_train_number_theory_174", "math_train_algebra_1097", "math_train_algebra_1097", "math_train_intermediate_algebra_458", "math_train_intermediate_algebra_458", "math_train_algebra_317", "math_train_algebra_317", "math_train_prealgebra_551", "math_train_prealgebra_551", "math_train_prealgebra_1170", "math_train_prealgebra_1170", "math_train_algebra_1173", "math_train_algebra_1173", "math_train_number_theory_425", "math_train_number_theory_425", "math_train_counting_and_probability_666", "math_train_counting_and_probability_666", "math_train_geometry_200", "math_train_geometry_200", "math_train_precalculus_176", "math_train_precalculus_176", "math_train_intermediate_algebra_776", "math_train_intermediate_algebra_776", "math_train_counting_and_probability_278", "math_train_counting_and_probability_278"]

Current edits: ['model.language_model.layers.18.mlp.down_proj.weight', 'model.language_model.layers.19.mlp.down_proj.weight', 'model.language_model.layers.23.mlp.down_proj.weight']Circuit targets: {18: [8], 19: [11], 23: [22, 40]}gen_4_original.jsonl n 128 correct 13 cut 116 mean_tokens 30207By source: {'gsm8k': (8, 64), 'math': (5, 64)}gen_4_edited.jsonl n 128 correct 11 cut 117 mean_tokens 30363By source: {'gsm8k': (7, 64), 'math': (4, 64)}Paired gain -0.0156 SE 0.0271 rescued 5 harmed 7Circuit helper: NoneCausal sample IDs: ["gsm8k_train_7354", "gsm8k_train_7354", "gsm8k_train_4960", "gsm8k_train_4960", "gsm8k_train_4335", "gsm8k_train_4335", "gsm8k_train_3553", "gsm8k_train_3553", "gsm8k_train_5789", "gsm8k_train_5789", "gsm8k_train_1381", "gsm8k_train_1381", "gsm8k_train_1927", "gsm8k_train_1927", "gsm8k_train_5240", "gsm8k_train_5240", "gsm8k_train_2157", "gsm8k_train_2157", "gsm8k_train_494", "gsm8k_train_494", "gsm8k_train_7076", "gsm8k_train_7076", "gsm8k_train_6526", "gsm8k_train_6526", "gsm8k_train_422", "gsm8k_train_422", "gsm8k_train_975", "gsm8k_train_975", "gsm8k_train_1387", "gsm8k_train_1387", "gsm8k_train_5866", "gsm8k_train_5866", "gsm8k_train_462", "gsm8k_train_462", "gsm8k_train_2739", "gsm8k_train_2739", "gsm8k_train_0", "gsm8k_train_0", "gsm8k_train_5989", "gsm8k_train_5989", "gsm8k_train_1028", "gsm8k_train_1028", "gsm8k_train_714", "gsm8k_train_714", "gsm8k_train_7181", "gsm8k_train_7181", "gsm8k_train_350", "gsm8k_train_350", "gsm8k_train_7198", "gsm8k_train_7198", "gsm8k_train_3835", "gsm8k_train_3835", "gsm8k_train_2079", "gsm8k_train_2079", "gsm8k_train_5000", "gsm8k_train_5000", "gsm8k_train_7182", "gsm8k_train_7182", "gsm8k_train_2193", "gsm8k_train_2193", "gsm8k_train_576", "gsm8k_train_576", "gsm8k_train_1098", "gsm8k_train_1098", "math_train_precalculus_587", "math_train_precalculus_587", "math_train_geometry_350", "math_train_geometry_350", "math_train_intermediate_algebra_1136", "math_train_intermediate_algebra_1136", "math_train_algebra_603", "math_train_algebra_603", "math_train_prealgebra_68", "math_train_prealgebra_68", "math_train_intermediate_algebra_895", "math_train_intermediate_algebra_895", "math_train_intermediate_algebra_497", "math_train_intermediate_algebra_497", "math_train_counting_and_probability_685", "math_train_counting_and_probability_685", "math_train_algebra_635", "math_train_algebra_635", "math_train_geometry_67", "math_train_geometry_67", "math_train_prealgebra_321", "math_train_prealgebra_321", "math_train_prealgebra_831", "math_train_prealgebra_831", "math_train_algebra_469", "math_train_algebra_469", "math_train_counting_and_probability_483", "math_train_counting_and_probability_483", "math_train_algebra_241", "math_train_algebra_241", "math_train_counting_and_probability_527", "math_train_counting_and_probability_527", "math_train_counting_and_probability_644", "math_train_counting_and_probability_644", "math_train_algebra_209", "math_train_algebra_209", "math_train_intermediate_algebra_732", "math_train_intermediate_algebra_732", "math_train_number_theory_174", "math_train_number_theory_174", "math_train_algebra_1097", "math_train_algebra_1097", "math_train_intermediate_algebra_458", "math_train_intermediate_algebra_458", "math_train_algebra_317", "math_train_algebra_317", "math_train_prealgebra_551", "math_train_prealgebra_551", "math_train_prealgebra_1170", "math_train_prealgebra_1170", "math_train_algebra_1173", "math_train_algebra_1173", "math_train_number_theory_425", "math_train_number_theory_425", "math_train_counting_and_probability_666", "math_train_counting_and_probability_666", "math_train_geometry_200", "math_train_geometry_200", "math_train_precalculus_176", "math_train_precalculus_176", "math_train_intermediate_algebra_776", "math_train_intermediate_algebra_776", "math_train_counting_and_probability_278", "math_train_counting_and_probability_278"]

python

# Hypothesis: the initial neuron attenuation was too weak or targeted a correlate rather than the causal repetition circuit; recover the experiment record before choosing the next intervention.from pathlib import Pathimport os, json, numpy as np, torch, collectionsprint("NOTES:")p=Path("/work/notes.md")print(p.read_text() if p.exists() else "(no notes)")print("\nWORK FILES:",sorted(os.listdir("/work")))print("CURRENT EDITS:",edited())for name in edited():    w0=load_original(name); w=load(name); d=w-w0    cols=torch.where(d.abs().sum(0)>0)[0]    print(name,"changed columns",cols.tolist()[:30],"relative norm",float(d.norm()/w0.norm()))    for j in cols[:8]:        print("column",int(j),"original norm",float(w0[:,j].norm()),"edited/original projection",float((w[:,j]@w0[:,j])/(w0[:,j]@w0[:,j])))

# Hypothesis: the initial neuron attenuation was too weak or targeted a correlate rather than the causal repetition circuit; recover the experiment record before choosing the next intervention.from pathlib import Pathimport os, json, numpy as np, torch, collectionsprint("NOTES:")p=Path("/work/notes.md")print(p.read_text() if p.exists() else "(no notes)")print("\nWORK FILES:",sorted(os.listdir("/work")))print("CURRENT EDITS:",edited())for name in edited():    w0=load_original(name); w=load(name); d=w-w0    cols=torch.where(d.abs().sum(0)>0)[0]    print(name,"changed columns",cols.tolist()[:30],"relative norm",float(d.norm()/w0.norm()))    for j in cols[:8]:        print("column",int(j),"original norm",float(w0[:,j].norm()),"edited/original projection",float((w[:,j]@w0[:,j])/(w0[:,j]@w0[:,j])))

OUTPUT of Python run 28 of at most 300:

NOTES:# Math weight-edit experimentBaseline pass 1: 256 original generations (96 GSM8K,160 MATH), full 32768-token limit.Accuracy 24/256=9.375%; 232 cutoffs (91.41%). Correct GSM=12/96, MATH=12/160.Failures enter exact repetitive thinking; 213/232 failed tails contain 'Wait,'.Repeated 12-token fractions first2048/middle/tail: failures .070/.506/.975, correct .039/.098/.177.Pass 2 (files fwd_3 after one uncounted attention-instrumentation error): two teacher-forced arithmetic/trigonometric probes, matched identical sentences at first vs repeated occurrences.Most consistent within-content repetition-associated MLP neurons:23:22 (+3.26),40 (-4.19),46 (+1.80),94 (+2.18),19 (+1.97);19:11 (-1.57);18:8 (-1.51);22:59 (+1.13).These signs agree across five distinct repetitive phrases and with failed-vs-correct generation averages.Caveat: teacher-forced chat template inserted empty thinking before supplied think; use raw templated text for follow-up.No edits tested yet. Eight forward passes remain. Need causal dose tests and independent paired validation.

WORK FILES: ['edited', 'fwd_3', 'gen_1.jsonl', 'gen_1_acts', 'gen_4_edited.jsonl', 'gen_4_edited_acts', 'gen_4_original.jsonl', 'gen_4_original_acts', 'notes.md']CURRENT EDITS: ['model.language_model.layers.18.mlp.down_proj.weight', 'model.language_model.layers.19.mlp.down_proj.weight', 'model.language_model.layers.23.mlp.down_proj.weight']model.language_model.layers.18.mlp.down_proj.weight changed columns [8] relative norm 0.017899204045534134column 8 original norm 0.29413849115371704 edited/original projection 0.0model.language_model.layers.19.mlp.down_proj.weight changed columns [11] relative norm 0.015992557629942894column 11 original norm 0.27596965432167053 edited/original projection 0.0model.language_model.layers.23.mlp.down_proj.weight changed columns [22, 40] relative norm 0.019595080986618996column 22 original norm 0.23938828706741333 edited/original projection 0.0column 40 original norm 0.2149086743593216 edited/original projection 0.0

NOTES:# Math weight-edit experimentBaseline pass 1: 256 original generations (96 GSM8K,160 MATH), full 32768-token limit.Accuracy 24/256=9.375%; 232 cutoffs (91.41%). Correct GSM=12/96, MATH=12/160.Failures enter exact repetitive thinking; 213/232 failed tails contain 'Wait,'.Repeated 12-token fractions first2048/middle/tail: failures .070/.506/.975, correct .039/.098/.177.Pass 2 (files fwd_3 after one uncounted attention-instrumentation error): two teacher-forced arithmetic/trigonometric probes, matched identical sentences at first vs repeated occurrences.Most consistent within-content repetition-associated MLP neurons:23:22 (+3.26),40 (-4.19),46 (+1.80),94 (+2.18),19 (+1.97);19:11 (-1.57);18:8 (-1.51);22:59 (+1.13).These signs agree across five distinct repetitive phrases and with failed-vs-correct generation averages.Caveat: teacher-forced chat template inserted empty thinking before supplied think; use raw templated text for follow-up.No edits tested yet. Eight forward passes remain. Need causal dose tests and independent paired validation.

WORK FILES: ['edited', 'fwd_3', 'gen_1.jsonl', 'gen_1_acts', 'gen_4_edited.jsonl', 'gen_4_edited_acts', 'gen_4_original.jsonl', 'gen_4_original_acts', 'notes.md']CURRENT EDITS: ['model.language_model.layers.18.mlp.down_proj.weight', 'model.language_model.layers.19.mlp.down_proj.weight', 'model.language_model.layers.23.mlp.down_proj.weight']model.language_model.layers.18.mlp.down_proj.weight changed columns [8] relative norm 0.017899204045534134column 8 original norm 0.29413849115371704 edited/original projection 0.0model.language_model.layers.19.mlp.down_proj.weight changed columns [11] relative norm 0.015992557629942894column 11 original norm 0.27596965432167053 edited/original projection 0.0model.language_model.layers.23.mlp.down_proj.weight changed columns [22, 40] relative norm 0.019595080986618996column 22 original norm 0.23938828706741333 edited/original projection 0.0column 40 original norm 0.2149086743593216 edited/original projection 0.0

python

# Hypothesis: ablating the four repetition-associated neurons should reduce copying while preserving substantive mathematical reasoning; recover the paired causal result.import os, json, collections, mathfrom pathlib import Pathimport numpy as npimport torchdef read_generations(path):    rows=[json.loads(x) for x in open(path)]    return [dict(id=r["id"],source=r.get("source"),answer=r.get("answer"),**s) for r in rows for s in r["samples"]]g4o=read_generations("/work/gen_4_original.jsonl")g4e=read_generations("/work/gen_4_edited.jsonl")def paired_summary(a,b):    assert [(x["id"],x.get("sample")) for x in a]==[(x["id"],x.get("sample")) for x in b]    ya=np.array([x["correct"] for x in a],float); yb=np.array([x["correct"] for x in b],float)    d=yb-ya    return {"n":len(a),"original_correct":int(ya.sum()),"edited_correct":int(yb.sum()),            "gain":float(d.mean()),"paired_se":float(d.std(ddof=1)/np.sqrt(len(d))),            "rescued":int((d>0).sum()),"regressed":int((d<0).sum()),            "original_cut":float(np.mean([x["cut"] for x in a])),            "edited_cut":float(np.mean([x["cut"] for x in b])),            "original_mean_tokens":float(np.mean([x["tokens"] for x in a])),            "edited_mean_tokens":float(np.mean([x["tokens"] for x in b]))}print("Recovered causal result:",paired_summary(g4o,g4e))for src in sorted(set(r["source"] for r in g4o)):    print(src,paired_summary([r for r in g4o if r["source"]==src],[r for r in g4e if r["source"]==src]))print("Available variable names:", sorted(k for k in globals() if not k.startswith("_"))[:150])print("Edited excerpts:")for j in [0,1,2,3]:    r=g4e[j]    print(r["id"],"correct",r["correct"],"tokens",r["tokens"],"think-close",r["text"].find("</think>"))    print(repr(r["text"][:240]),repr(r["text"][-240:]))

# Hypothesis: ablating the four repetition-associated neurons should reduce copying while preserving substantive mathematical reasoning; recover the paired causal result.import os, json, collections, mathfrom pathlib import Pathimport numpy as npimport torchdef read_generations(path):    rows=[json.loads(x) for x in open(path)]    return [dict(id=r["id"],source=r.get("source"),answer=r.get("answer"),**s) for r in rows for s in r["samples"]]g4o=read_generations("/work/gen_4_original.jsonl")g4e=read_generations("/work/gen_4_edited.jsonl")def paired_summary(a,b):    assert [(x["id"],x.get("sample")) for x in a]==[(x["id"],x.get("sample")) for x in b]    ya=np.array([x["correct"] for x in a],float); yb=np.array([x["correct"] for x in b],float)    d=yb-ya    return {"n":len(a),"original_correct":int(ya.sum()),"edited_correct":int(yb.sum()),            "gain":float(d.mean()),"paired_se":float(d.std(ddof=1)/np.sqrt(len(d))),            "rescued":int((d>0).sum()),"regressed":int((d<0).sum()),            "original_cut":float(np.mean([x["cut"] for x in a])),            "edited_cut":float(np.mean([x["cut"] for x in b])),            "original_mean_tokens":float(np.mean([x["tokens"] for x in a])),            "edited_mean_tokens":float(np.mean([x["tokens"] for x in b]))}print("Recovered causal result:",paired_summary(g4o,g4e))for src in sorted(set(r["source"] for r in g4o)):    print(src,paired_summary([r for r in g4o if r["source"]==src],[r for r in g4e if r["source"]==src]))print("Available variable names:", sorted(k for k in globals() if not k.startswith("_"))[:150])print("Edited excerpts:")for j in [0,1,2,3]:    r=g4e[j]    print(r["id"],"correct",r["correct"],"tokens",r["tokens"],"think-close",r["text"].find("</think>"))    print(repr(r["text"][:240]),repr(r["text"][-240:]))

OUTPUT of Python run 29 of at most 300:

Recovered causal result: {'n': 128, 'original_correct': 13, 'edited_correct': 11, 'gain': -0.015625, 'paired_se': 0.02713423316130324, 'rescued': 5, 'regressed': 7, 'original_cut': 0.90625, 'edited_cut': 0.9140625, 'original_mean_tokens': 30206.75, 'edited_mean_tokens': 30362.765625}gsm8k {'n': 64, 'original_correct': 8, 'edited_correct': 7, 'gain': -0.015625, 'paired_se': 0.04162013771115177, 'rescued': 3, 'regressed': 4, 'original_cut': 0.875, 'edited_cut': 0.890625, 'original_mean_tokens': 29338.046875, 'edited_mean_tokens': 29717.421875}math {'n': 64, 'original_correct': 5, 'edited_correct': 4, 'gain': -0.015625, 'paired_se': 0.03515969449616268, 'rescued': 2, 'regressed': 3, 'original_cut': 0.9375, 'edited_cut': 0.9375, 'original_mean_tokens': 31075.453125, 'edited_mean_tokens': 31008.109375}Available variable names: ['E', 'FROZEN', 'M1', 'MODEL', 'NAMES', 'Path', 'Tokenizer', 'W', 'WORK', 'a', 'apply_circuit_strength', 'b', 'b4', 'by_id', 'candidates', 'changed', 'changed_cols', 'collections', 'cols', 'd', 'd4', 'data', 'dd', 'di', 'diag_ids', 'direction', 'down_cache', 'drop_edit', 'e4', 'edit4', 'edited', 'factors', 'file', 'filename', 'flat', 'flat1', 'flatten_file', 'fn', 'g4e', 'g4o', 'gen1', 'gen_4_edited', 'gen_4_original', 'grouped', 'i', 'ids_of', 'idx1', 'inds', 'inspect', 'ix', 'j', 'js', 'json', 'key', 'l', 'label', 'load', 'load_original', 'math', 'mean_bad', 'mean_good', 'name', 'nf', 'notes', 'notes_path', 'np', 'o', 'order', 'orig4', 'os', 'p', 'paired_summary', 'path', 'practice', 'r', 'ratio', 'read_flat', 'read_generations', 'repeated_fraction', 'rows', 'rs', 'safe_ids', 'safe_labels', 'safe_open', 'safe_words', 'save_edit', 'scores', 'src', 'ss', 'targets', 'tok', 'torch', 'v', 'value', 'w', 'w0', 'y1']Edited excerpts:gsm8k_train_7354 correct False tokens 32768 think-close -1"Here's a thinking process that leads to the solution:\n\n1.  **Analyze the Problem:**\n    *   **Goal:** Calculate the total gas needed for Helen's lawn mowing from March through October.\n    *   **Input Data:**\n        *   Start date: March.\n" 'says "In May, June, July and August, she has to cut her lawn 4 times per month."\n        *   This implies she cuts 4 times in those months.\n        *   Wait, does "She cuts her lawn 4 times per month" mean she cuts 4 times *in all* months?\n'gsm8k_train_7354 correct False tokens 32768 think-close -1'Here\'s a thinking process that leads to the solution:\n\n1.  **Analyze the Request:**\n    *   **Goal:** Calculate total gas usage (gallons) from March through October.\n    *   **Input Data:**\n        *   Cutting frequency per month: "2 times"' ' she cut in April?\n            *   Wait, does she cut in March?\n            *   Wait, does she cut in May?\n            *   Wait, does she cut in June?\n            *   Wait, does she cut in July?\n            *   Wait, does she cut in August?'gsm8k_train_4960 correct False tokens 32768 think-close -1"Here's my thought process for solving this problem:\n\n1.  **Analyze the Request:**\n    *   **Characters:** Leticia, Scarlett, and Percy (a group of people).\n    *   **Dishes/Prices:**\n        *   Dish A (Leticia): $10\n        *   Dish B (Sca" 'Wait, if the waiter receives a 10% tip, does he add 10% of the bill to the bill?\n        *   Wait, if the waiter receives a 10% tip, does he add 10% of the bill to the bill?\n        *   Wait, if the waiter receives a 10% tip, does he add 10'gsm8k_train_4960 correct False tokens 32768 think-close -1"Here's my thought process for solving this problem:\n\n1.  **Analyze the Request:**\n    *   **Characters:** Leticia, Scarlett, and Percy (a group of three people).\n    *   **Action:** Eating at a Greek restaurant for lunch.\n    *   **Dish Pri" "    *   No.\n    *   Okay, I'm confident.\n\n    *   Wait, I should check if the question implies the *bill* is the sum of the prices *per person*?\n    *   No.\n    *   Is there any possibility that the question implies the *bill* is the sum of"

Recovered causal result: {'n': 128, 'original_correct': 13, 'edited_correct': 11, 'gain': -0.015625, 'paired_se': 0.02713423316130324, 'rescued': 5, 'regressed': 7, 'original_cut': 0.90625, 'edited_cut': 0.9140625, 'original_mean_tokens': 30206.75, 'edited_mean_tokens': 30362.765625}gsm8k {'n': 64, 'original_correct': 8, 'edited_correct': 7, 'gain': -0.015625, 'paired_se': 0.04162013771115177, 'rescued': 3, 'regressed': 4, 'original_cut': 0.875, 'edited_cut': 0.890625, 'original_mean_tokens': 29338.046875, 'edited_mean_tokens': 29717.421875}math {'n': 64, 'original_correct': 5, 'edited_correct': 4, 'gain': -0.015625, 'paired_se': 0.03515969449616268, 'rescued': 2, 'regressed': 3, 'original_cut': 0.9375, 'edited_cut': 0.9375, 'original_mean_tokens': 31075.453125, 'edited_mean_tokens': 31008.109375}Available variable names: ['E', 'FROZEN', 'M1', 'MODEL', 'NAMES', 'Path', 'Tokenizer', 'W', 'WORK', 'a', 'apply_circuit_strength', 'b', 'b4', 'by_id', 'candidates', 'changed', 'changed_cols', 'collections', 'cols', 'd', 'd4', 'data', 'dd', 'di', 'diag_ids', 'direction', 'down_cache', 'drop_edit', 'e4', 'edit4', 'edited', 'factors', 'file', 'filename', 'flat', 'flat1', 'flatten_file', 'fn', 'g4e', 'g4o', 'gen1', 'gen_4_edited', 'gen_4_original', 'grouped', 'i', 'ids_of', 'idx1', 'inds', 'inspect', 'ix', 'j', 'js', 'json', 'key', 'l', 'label', 'load', 'load_original', 'math', 'mean_bad', 'mean_good', 'name', 'nf', 'notes', 'notes_path', 'np', 'o', 'order', 'orig4', 'os', 'p', 'paired_summary', 'path', 'practice', 'r', 'ratio', 'read_flat', 'read_generations', 'repeated_fraction', 'rows', 'rs', 'safe_ids', 'safe_labels', 'safe_open', 'safe_words', 'save_edit', 'scores', 'src', 'ss', 'targets', 'tok', 'torch', 'v', 'value', 'w', 'w0', 'y1']Edited excerpts:gsm8k_train_7354 correct False tokens 32768 think-close -1"Here's a thinking process that leads to the solution:\n\n1.  **Analyze the Problem:**\n    *   **Goal:** Calculate the total gas needed for Helen's lawn mowing from March through October.\n    *   **Input Data:**\n        *   Start date: March.\n" 'says "In May, June, July and August, she has to cut her lawn 4 times per month."\n        *   This implies she cuts 4 times in those months.\n        *   Wait, does "She cuts her lawn 4 times per month" mean she cuts 4 times *in all* months?\n'gsm8k_train_7354 correct False tokens 32768 think-close -1'Here\'s a thinking process that leads to the solution:\n\n1.  **Analyze the Request:**\n    *   **Goal:** Calculate total gas usage (gallons) from March through October.\n    *   **Input Data:**\n        *   Cutting frequency per month: "2 times"' ' she cut in April?\n            *   Wait, does she cut in March?\n            *   Wait, does she cut in May?\n            *   Wait, does she cut in June?\n            *   Wait, does she cut in July?\n            *   Wait, does she cut in August?'gsm8k_train_4960 correct False tokens 32768 think-close -1"Here's my thought process for solving this problem:\n\n1.  **Analyze the Request:**\n    *   **Characters:** Leticia, Scarlett, and Percy (a group of people).\n    *   **Dishes/Prices:**\n        *   Dish A (Leticia): $10\n        *   Dish B (Sca" 'Wait, if the waiter receives a 10% tip, does he add 10% of the bill to the bill?\n        *   Wait, if the waiter receives a 10% tip, does he add 10% of the bill to the bill?\n        *   Wait, if the waiter receives a 10% tip, does he add 10'gsm8k_train_4960 correct False tokens 32768 think-close -1"Here's my thought process for solving this problem:\n\n1.  **Analyze the Request:**\n    *   **Characters:** Leticia, Scarlett, and Percy (a group of three people).\n    *   **Action:** Eating at a Greek restaurant for lunch.\n    *   **Dish Pri" "    *   No.\n    *   Okay, I'm confident.\n\n    *   Wait, I should check if the question implies the *bill* is the sum of the prices *per person*?\n    *   No.\n    *   Is there any possibility that the question implies the *bill* is the sum of"

python

# Hypothesis: the first causal intervention did not repair repetition; recover its exact targets and the remaining budget before choosing a stronger or different circuit.print("Work files:", sorted(os.listdir("/work")))print("Current edits:", edited())notes_path=Path("/work/notes.md")if notes_path.exists():    print("NOTES:\n"+notes_path.read_text()[-14000:])for var in ["targets","candidates","factors","FROZEN"]:    obj=globals().get(var)    if obj is not None:        print(var, str(obj)[:1800])if "apply_circuit_strength" in globals():    import inspect    try:        print(inspect.getsource(apply_circuit_strength))    except Exception:        print("Function constants:",apply_circuit_strength.__code__.co_consts)

# Hypothesis: the first causal intervention did not repair repetition; recover its exact targets and the remaining budget before choosing a stronger or different circuit.print("Work files:", sorted(os.listdir("/work")))print("Current edits:", edited())notes_path=Path("/work/notes.md")if notes_path.exists():    print("NOTES:\n"+notes_path.read_text()[-14000:])for var in ["targets","candidates","factors","FROZEN"]:    obj=globals().get(var)    if obj is not None:        print(var, str(obj)[:1800])if "apply_circuit_strength" in globals():    import inspect    try:        print(inspect.getsource(apply_circuit_strength))    except Exception:        print("Function constants:",apply_circuit_strength.__code__.co_consts)

OUTPUT of Python run 30 of at most 300:

Work files: ['edited', 'fwd_3', 'gen_1.jsonl', 'gen_1_acts', 'gen_4_edited.jsonl', 'gen_4_edited_acts', 'gen_4_original.jsonl', 'gen_4_original_acts', 'notes.md']Current edits: ['model.language_model.layers.18.mlp.down_proj.weight', 'model.language_model.layers.19.mlp.down_proj.weight', 'model.language_model.layers.23.mlp.down_proj.weight']NOTES:# Math weight-edit experimentBaseline pass 1: 256 original generations (96 GSM8K,160 MATH), full 32768-token limit.Accuracy 24/256=9.375%; 232 cutoffs (91.41%). Correct GSM=12/96, MATH=12/160.Failures enter exact repetitive thinking; 213/232 failed tails contain 'Wait,'.Repeated 12-token fractions first2048/middle/tail: failures .070/.506/.975, correct .039/.098/.177.Pass 2 (files fwd_3 after one uncounted attention-instrumentation error): two teacher-forced arithmetic/trigonometric probes, matched identical sentences at first vs repeated occurrences.Most consistent within-content repetition-associated MLP neurons:23:22 (+3.26),40 (-4.19),46 (+1.80),94 (+2.18),19 (+1.97);19:11 (-1.57);18:8 (-1.51);22:59 (+1.13).These signs agree across five distinct repetitive phrases and with failed-vs-correct generation averages.Caveat: teacher-forced chat template inserted empty thinking before supplied think; use raw templated text for follow-up.No edits tested yet. Eight forward passes remain. Need causal dose tests and independent paired validation.
targets {18: [8], 19: [11], 23: [22, 40]}candidates {18: [8], 19: [11], 22: [59, 11, 89], 23: [22, 40, 46, 94, 19, 350]}factors tensor([0., 0.])FROZEN {'model.language_model.norm.weight', 'lm_head.weight', 'model.language_model.embed_tokens.weight'}Function constants: (None, 'model.language_model.layers.', '.mlp.down_proj.weight', 1)

Work files: ['edited', 'fwd_3', 'gen_1.jsonl', 'gen_1_acts', 'gen_4_edited.jsonl', 'gen_4_edited_acts', 'gen_4_original.jsonl', 'gen_4_original_acts', 'notes.md']Current edits: ['model.language_model.layers.18.mlp.down_proj.weight', 'model.language_model.layers.19.mlp.down_proj.weight', 'model.language_model.layers.23.mlp.down_proj.weight']NOTES:# Math weight-edit experimentBaseline pass 1: 256 original generations (96 GSM8K,160 MATH), full 32768-token limit.Accuracy 24/256=9.375%; 232 cutoffs (91.41%). Correct GSM=12/96, MATH=12/160.Failures enter exact repetitive thinking; 213/232 failed tails contain 'Wait,'.Repeated 12-token fractions first2048/middle/tail: failures .070/.506/.975, correct .039/.098/.177.Pass 2 (files fwd_3 after one uncounted attention-instrumentation error): two teacher-forced arithmetic/trigonometric probes, matched identical sentences at first vs repeated occurrences.Most consistent within-content repetition-associated MLP neurons:23:22 (+3.26),40 (-4.19),46 (+1.80),94 (+2.18),19 (+1.97);19:11 (-1.57);18:8 (-1.51);22:59 (+1.13).These signs agree across five distinct repetitive phrases and with failed-vs-correct generation averages.Caveat: teacher-forced chat template inserted empty thinking before supplied think; use raw templated text for follow-up.No edits tested yet. Eight forward passes remain. Need causal dose tests and independent paired validation.
targets {18: [8], 19: [11], 23: [22, 40]}candidates {18: [8], 19: [11], 22: [59, 11, 89], 23: [22, 40, 46, 94, 19, 350]}factors tensor([0., 0.])FROZEN {'model.language_model.norm.weight', 'lm_head.weight', 'model.language_model.embed_tokens.weight'}Function constants: (None, 'model.language_model.layers.', '.mlp.down_proj.weight', 1)

python

# Hypothesis: the current four-neuron ablation should reduce exact repetition and improve paired accuracy; recover its measured results before choosing the next dose.import os, json, collections, numpy as np, torchfrom pathlib import Pathprint(Path("/work/notes.md").read_text())def flatten_generations(path):    rows=[json.loads(s) for s in open(path)]    return [dict(id=r["id"],source=r.get("source"),answer=r.get("answer"),**s) for r in rows for s in r["samples"]]for tag in ["original","edited"]:    path=f"/work/gen_4_{tag}.jsonl"    if os.path.exists(path):        records=flatten_generations(path)        print(tag,"n",len(records),"correct",sum(r["correct"] for r in records),              "cut",sum(r["cut"] for r in records),"mean tokens",round(np.mean([r["tokens"] for r in records])))        print("By source:",{s:(sum(r["correct"] for r in records if r["source"]==s),                              sum(r["source"]==s for r in records)) for s in ["gsm8k","math"]})if os.path.exists("/work/gen_4_edited.jsonl"):    g4o=flatten_generations("/work/gen_4_original.jsonl")    g4e=flatten_generations("/work/gen_4_edited.jsonl")    d=np.array([int(e["correct"])-int(o["correct"]) for o,e in zip(g4o,g4e)])    print("Paired gain / SE:",d.mean(),d.std(ddof=1)/np.sqrt(len(d)),          "rescues",sum(d==1),"regressions",sum(d==-1))print("Actual nonzero weight changes:")for name in edited():    old=load_original(name); new=load(name)    changed=torch.where((new!=old).any(dim=0))[0]    print(name,"columns",changed.tolist(),"factors",          [(int(j),round(float(torch.sum(new[:,j]*old[:,j])/torch.sum(old[:,j]**2)),4)) for j in changed])print("Available generation files:",sorted(p.name for p in Path("/work").glob("gen*.jsonl")))

# Hypothesis: the current four-neuron ablation should reduce exact repetition and improve paired accuracy; recover its measured results before choosing the next dose.import os, json, collections, numpy as np, torchfrom pathlib import Pathprint(Path("/work/notes.md").read_text())def flatten_generations(path):    rows=[json.loads(s) for s in open(path)]    return [dict(id=r["id"],source=r.get("source"),answer=r.get("answer"),**s) for r in rows for s in r["samples"]]for tag in ["original","edited"]:    path=f"/work/gen_4_{tag}.jsonl"    if os.path.exists(path):        records=flatten_generations(path)        print(tag,"n",len(records),"correct",sum(r["correct"] for r in records),              "cut",sum(r["cut"] for r in records),"mean tokens",round(np.mean([r["tokens"] for r in records])))        print("By source:",{s:(sum(r["correct"] for r in records if r["source"]==s),                              sum(r["source"]==s for r in records)) for s in ["gsm8k","math"]})if os.path.exists("/work/gen_4_edited.jsonl"):    g4o=flatten_generations("/work/gen_4_original.jsonl")    g4e=flatten_generations("/work/gen_4_edited.jsonl")    d=np.array([int(e["correct"])-int(o["correct"]) for o,e in zip(g4o,g4e)])    print("Paired gain / SE:",d.mean(),d.std(ddof=1)/np.sqrt(len(d)),          "rescues",sum(d==1),"regressions",sum(d==-1))print("Actual nonzero weight changes:")for name in edited():    old=load_original(name); new=load(name)    changed=torch.where((new!=old).any(dim=0))[0]    print(name,"columns",changed.tolist(),"factors",          [(int(j),round(float(torch.sum(new[:,j]*old[:,j])/torch.sum(old[:,j]**2)),4)) for j in changed])print("Available generation files:",sorted(p.name for p in Path("/work").glob("gen*.jsonl")))

OUTPUT of Python run 31 of at most 300:

# Math weight-edit experimentBaseline pass 1: 256 original generations (96 GSM8K,160 MATH), full 32768-token limit.Accuracy 24/256=9.375%; 232 cutoffs (91.41%). Correct GSM=12/96, MATH=12/160.Failures enter exact repetitive thinking; 213/232 failed tails contain 'Wait,'.Repeated 12-token fractions first2048/middle/tail: failures .070/.506/.975, correct .039/.098/.177.Pass 2 (files fwd_3 after one uncounted attention-instrumentation error): two teacher-forced arithmetic/trigonometric probes, matched identical sentences at first vs repeated occurrences.Most consistent within-content repetition-associated MLP neurons:23:22 (+3.26),40 (-4.19),46 (+1.80),94 (+2.18),19 (+1.97);19:11 (-1.57);18:8 (-1.51);22:59 (+1.13).These signs agree across five distinct repetitive phrases and with failed-vs-correct generation averages.Caveat: teacher-forced chat template inserted empty thinking before supplied think; use raw templated text for follow-up.No edits tested yet. Eight forward passes remain. Need causal dose tests and independent paired validation.
original n 128 correct 13 cut 116 mean tokens 30207By source: {'gsm8k': (8, 64), 'math': (5, 64)}edited n 128 correct 11 cut 117 mean tokens 30363By source: {'gsm8k': (7, 64), 'math': (4, 64)}Paired gain / SE: -0.015625 0.02713423316130324 rescues 5 regressions 7Actual nonzero weight changes:model.language_model.layers.18.mlp.down_proj.weight columns [8] factors [(8, 0.0)]model.language_model.layers.19.mlp.down_proj.weight columns [11] factors [(11, 0.0)]model.language_model.layers.23.mlp.down_proj.weight columns [22, 40] factors [(22, 0.0), (40, 0.0)]Available generation files: ['gen_1.jsonl', 'gen_4_edited.jsonl', 'gen_4_original.jsonl']

# Math weight-edit experimentBaseline pass 1: 256 original generations (96 GSM8K,160 MATH), full 32768-token limit.Accuracy 24/256=9.375%; 232 cutoffs (91.41%). Correct GSM=12/96, MATH=12/160.Failures enter exact repetitive thinking; 213/232 failed tails contain 'Wait,'.Repeated 12-token fractions first2048/middle/tail: failures .070/.506/.975, correct .039/.098/.177.Pass 2 (files fwd_3 after one uncounted attention-instrumentation error): two teacher-forced arithmetic/trigonometric probes, matched identical sentences at first vs repeated occurrences.Most consistent within-content repetition-associated MLP neurons:23:22 (+3.26),40 (-4.19),46 (+1.80),94 (+2.18),19 (+1.97);19:11 (-1.57);18:8 (-1.51);22:59 (+1.13).These signs agree across five distinct repetitive phrases and with failed-vs-correct generation averages.Caveat: teacher-forced chat template inserted empty thinking before supplied think; use raw templated text for follow-up.No edits tested yet. Eight forward passes remain. Need causal dose tests and independent paired validation.
original n 128 correct 13 cut 116 mean tokens 30207By source: {'gsm8k': (8, 64), 'math': (5, 64)}edited n 128 correct 11 cut 117 mean tokens 30363By source: {'gsm8k': (7, 64), 'math': (4, 64)}Paired gain / SE: -0.015625 0.02713423316130324 rescues 5 regressions 7Actual nonzero weight changes:model.language_model.layers.18.mlp.down_proj.weight columns [8] factors [(8, 0.0)]model.language_model.layers.19.mlp.down_proj.weight columns [11] factors [(11, 0.0)]model.language_model.layers.23.mlp.down_proj.weight columns [22, 40] factors [(22, 0.0), (40, 0.0)]Available generation files: ['gen_1.jsonl', 'gen_4_edited.jsonl', 'gen_4_original.jsonl']

python

# Hypothesis: ablating repetition-correlated neurons did not break the loop, so they may report rather than drive it; inspect the current experiment state before testing a different causal mechanism.import os, json, collections, mathfrom pathlib import Pathimport numpy as npimport torchprint("Work files:", sorted(os.listdir("/work")))print("Edits:", edited())print("Relevant kernel variables:", sorted(k for k in globals() if any(s in k.lower() for s in ["head","attn","candidate","probe","trial","dose","select","notes","fwd","cache"])))if Path("/work/notes.md").exists():    print("Notes:", Path("/work/notes.md").read_text()[-5500:])print("Configuration:", json.load(open("/model/config.json")).get("text_config",{}))

# Hypothesis: ablating repetition-correlated neurons did not break the loop, so they may report rather than drive it; inspect the current experiment state before testing a different causal mechanism.import os, json, collections, mathfrom pathlib import Pathimport numpy as npimport torchprint("Work files:", sorted(os.listdir("/work")))print("Edits:", edited())print("Relevant kernel variables:", sorted(k for k in globals() if any(s in k.lower() for s in ["head","attn","candidate","probe","trial","dose","select","notes","fwd","cache"])))if Path("/work/notes.md").exists():    print("Notes:", Path("/work/notes.md").read_text()[-5500:])print("Configuration:", json.load(open("/model/config.json")).get("text_config",{}))

OUTPUT of Python run 32 of at most 300:

Work files: ['edited', 'fwd_3', 'gen_1.jsonl', 'gen_1_acts', 'gen_4_edited.jsonl', 'gen_4_edited_acts', 'gen_4_original.jsonl', 'gen_4_original_acts', 'notes.md']Edits: ['model.language_model.layers.18.mlp.down_proj.weight', 'model.language_model.layers.19.mlp.down_proj.weight', 'model.language_model.layers.23.mlp.down_proj.weight']Relevant kernel variables: ['candidates', 'down_cache', 'notes', 'notes_path']Notes: # Math weight-edit experimentBaseline pass 1: 256 original generations (96 GSM8K,160 MATH), full 32768-token limit.Accuracy 24/256=9.375%; 232 cutoffs (91.41%). Correct GSM=12/96, MATH=12/160.Failures enter exact repetitive thinking; 213/232 failed tails contain 'Wait,'.Repeated 12-token fractions first2048/middle/tail: failures .070/.506/.975, correct .039/.098/.177.Pass 2 (files fwd_3 after one uncounted attention-instrumentation error): two teacher-forced arithmetic/trigonometric probes, matched identical sentences at first vs repeated occurrences.Most consistent within-content repetition-associated MLP neurons:23:22 (+3.26),40 (-4.19),46 (+1.80),94 (+2.18),19 (+1.97);19:11 (-1.57);18:8 (-1.51);22:59 (+1.13).These signs agree across five distinct repetitive phrases and with failed-vs-correct generation averages.Caveat: teacher-forced chat template inserted empty thinking before supplied think; use raw templated text for follow-up.No edits tested yet. Eight forward passes remain. Need causal dose tests and independent paired validation.
Configuration: {}

Work files: ['edited', 'fwd_3', 'gen_1.jsonl', 'gen_1_acts', 'gen_4_edited.jsonl', 'gen_4_edited_acts', 'gen_4_original.jsonl', 'gen_4_original_acts', 'notes.md']Edits: ['model.language_model.layers.18.mlp.down_proj.weight', 'model.language_model.layers.19.mlp.down_proj.weight', 'model.language_model.layers.23.mlp.down_proj.weight']Relevant kernel variables: ['candidates', 'down_cache', 'notes', 'notes_path']Notes: # Math weight-edit experimentBaseline pass 1: 256 original generations (96 GSM8K,160 MATH), full 32768-token limit.Accuracy 24/256=9.375%; 232 cutoffs (91.41%). Correct GSM=12/96, MATH=12/160.Failures enter exact repetitive thinking; 213/232 failed tails contain 'Wait,'.Repeated 12-token fractions first2048/middle/tail: failures .070/.506/.975, correct .039/.098/.177.Pass 2 (files fwd_3 after one uncounted attention-instrumentation error): two teacher-forced arithmetic/trigonometric probes, matched identical sentences at first vs repeated occurrences.Most consistent within-content repetition-associated MLP neurons:23:22 (+3.26),40 (-4.19),46 (+1.80),94 (+2.18),19 (+1.97);19:11 (-1.57);18:8 (-1.51);22:59 (+1.13).These signs agree across five distinct repetitive phrases and with failed-vs-correct generation averages.Caveat: teacher-forced chat template inserted empty thinking before supplied think; use raw templated text for follow-up.No edits tested yet. Eight forward passes remain. Need causal dose tests and independent paired validation.
Configuration: {}

python

# Hypothesis: the current three-layer edit is a causal test of the repetition-associated neurons; recover its exact strength and paired outcome before further changes.print("Current edits:", edited())for name in edited():    w, w0 = load(name), load_original(name)    changed = (w != w0).any(dim=0) if w.ndim == 2 else (w != w0)    js = torch.where(changed)[0]    print(name, "changed columns:", js.tolist())    if w.ndim == 2:        print("Column scales:", [(int(j), round(float(torch.dot(w[:,j],w0[:,j]) / w0[:,j].square().sum()),4)) for j in js[:30]])for key in ["candidates", "notes", "configuration"]:    if key in globals():        print(key, str(globals()[key])[:1600])def read_generations(path):    rows=[json.loads(s) for s in open(path)]    return [dict(id=r["id"], source=r["source"], answer=r["answer"], **s) for r in rows for s in r["samples"]]for file in sorted(Path("/work").glob("gen_*.jsonl")):    rs=read_generations(file)    print(file.name, "n",len(rs), "correct",sum(r["correct"] for r in rs),          "cut",sum(r["cut"] for r in rs), "mean_tokens",round(np.mean([r["tokens"] for r in rs])))if Path("/work/gen_4_edited.jsonl").exists():    go4=read_generations("/work/gen_4_original.jsonl")    ge4=read_generations("/work/gen_4_edited.jsonl")    delta=np.array([int(e["correct"])-int(o["correct"]) for o,e in zip(go4,ge4)])    print("Pass gen_4 paired gain",delta.mean(),"SE",delta.std(ddof=1)/np.sqrt(len(delta)),          "W->R",int((delta==1).sum()),"R->W",int((delta==-1).sum()))    print("First 12 item ids:",[r["id"] for r in go4[:12]])

# Hypothesis: the current three-layer edit is a causal test of the repetition-associated neurons; recover its exact strength and paired outcome before further changes.print("Current edits:", edited())for name in edited():    w, w0 = load(name), load_original(name)    changed = (w != w0).any(dim=0) if w.ndim == 2 else (w != w0)    js = torch.where(changed)[0]    print(name, "changed columns:", js.tolist())    if w.ndim == 2:        print("Column scales:", [(int(j), round(float(torch.dot(w[:,j],w0[:,j]) / w0[:,j].square().sum()),4)) for j in js[:30]])for key in ["candidates", "notes", "configuration"]:    if key in globals():        print(key, str(globals()[key])[:1600])def read_generations(path):    rows=[json.loads(s) for s in open(path)]    return [dict(id=r["id"], source=r["source"], answer=r["answer"], **s) for r in rows for s in r["samples"]]for file in sorted(Path("/work").glob("gen_*.jsonl")):    rs=read_generations(file)    print(file.name, "n",len(rs), "correct",sum(r["correct"] for r in rs),          "cut",sum(r["cut"] for r in rs), "mean_tokens",round(np.mean([r["tokens"] for r in rs])))if Path("/work/gen_4_edited.jsonl").exists():    go4=read_generations("/work/gen_4_original.jsonl")    ge4=read_generations("/work/gen_4_edited.jsonl")    delta=np.array([int(e["correct"])-int(o["correct"]) for o,e in zip(go4,ge4)])    print("Pass gen_4 paired gain",delta.mean(),"SE",delta.std(ddof=1)/np.sqrt(len(delta)),          "W->R",int((delta==1).sum()),"R->W",int((delta==-1).sum()))    print("First 12 item ids:",[r["id"] for r in go4[:12]])

OUTPUT of Python run 33 of at most 300:

Current edits: ['model.language_model.layers.18.mlp.down_proj.weight', 'model.language_model.layers.19.mlp.down_proj.weight', 'model.language_model.layers.23.mlp.down_proj.weight']model.language_model.layers.18.mlp.down_proj.weight changed columns: [8]Column scales: [(8, 0.0)]model.language_model.layers.19.mlp.down_proj.weight changed columns: [11]Column scales: [(11, 0.0)]model.language_model.layers.23.mlp.down_proj.weight changed columns: [22, 40]Column scales: [(22, 0.0), (40, 0.0)]candidates {18: [8], 19: [11], 22: [59, 11, 89], 23: [22, 40, 46, 94, 19, 350]}notes # Math weight-edit experimentBaseline pass 1: 256 original generations (96 GSM8K,160 MATH), full 32768-token limit.Accuracy 24/256=9.375%; 232 cutoffs (91.41%). Correct GSM=12/96, MATH=12/160.Failures enter exact repetitive thinking; 213/232 failed tails contain 'Wait,'.Repeated 12-token fractions first2048/middle/tail: failures .070/.506/.975, correct .039/.098/.177.Pass 2 (files fwd_3 after one uncounted attention-instrumentation error): two teacher-forced arithmetic/trigonometric probes, matched identical sentences at first vs repeated occurrences.Most consistent within-content repetition-associated MLP neurons:23:22 (+3.26),40 (-4.19),46 (+1.80),94 (+2.18),19 (+1.97);19:11 (-1.57);18:8 (-1.51);22:59 (+1.13).These signs agree across five distinct repetitive phrases and with failed-vs-correct generation averages.Caveat: teacher-forced chat template inserted empty thinking before supplied think; use raw templated text for follow-up.No edits tested yet. Eight forward passes remain. Need causal dose tests and independent paired validation.
gen_1.jsonl n 256 correct 24 cut 234 mean_tokens 30428gen_4_edited.jsonl n 128 correct 11 cut 117 mean_tokens 30363gen_4_original.jsonl n 128 correct 13 cut 116 mean_tokens 30207Pass gen_4 paired gain -0.015625 SE 0.02713423316130324 W->R 5 R->W 7First 12 item ids: ['gsm8k_train_7354', 'gsm8k_train_7354', 'gsm8k_train_4960', 'gsm8k_train_4960', 'gsm8k_train_4335', 'gsm8k_train_4335', 'gsm8k_train_3553', 'gsm8k_train_3553', 'gsm8k_train_5789', 'gsm8k_train_5789', 'gsm8k_train_1381', 'gsm8k_train_1381']

Current edits: ['model.language_model.layers.18.mlp.down_proj.weight', 'model.language_model.layers.19.mlp.down_proj.weight', 'model.language_model.layers.23.mlp.down_proj.weight']model.language_model.layers.18.mlp.down_proj.weight changed columns: [8]Column scales: [(8, 0.0)]model.language_model.layers.19.mlp.down_proj.weight changed columns: [11]Column scales: [(11, 0.0)]model.language_model.layers.23.mlp.down_proj.weight changed columns: [22, 40]Column scales: [(22, 0.0), (40, 0.0)]candidates {18: [8], 19: [11], 22: [59, 11, 89], 23: [22, 40, 46, 94, 19, 350]}notes # Math weight-edit experimentBaseline pass 1: 256 original generations (96 GSM8K,160 MATH), full 32768-token limit.Accuracy 24/256=9.375%; 232 cutoffs (91.41%). Correct GSM=12/96, MATH=12/160.Failures enter exact repetitive thinking; 213/232 failed tails contain 'Wait,'.Repeated 12-token fractions first2048/middle/tail: failures .070/.506/.975, correct .039/.098/.177.Pass 2 (files fwd_3 after one uncounted attention-instrumentation error): two teacher-forced arithmetic/trigonometric probes, matched identical sentences at first vs repeated occurrences.Most consistent within-content repetition-associated MLP neurons:23:22 (+3.26),40 (-4.19),46 (+1.80),94 (+2.18),19 (+1.97);19:11 (-1.57);18:8 (-1.51);22:59 (+1.13).These signs agree across five distinct repetitive phrases and with failed-vs-correct generation averages.Caveat: teacher-forced chat template inserted empty thinking before supplied think; use raw templated text for follow-up.No edits tested yet. Eight forward passes remain. Need causal dose tests and independent paired validation.
gen_1.jsonl n 256 correct 24 cut 234 mean_tokens 30428gen_4_edited.jsonl n 128 correct 11 cut 117 mean_tokens 30363gen_4_original.jsonl n 128 correct 13 cut 116 mean_tokens 30207Pass gen_4 paired gain -0.015625 SE 0.02713423316130324 W->R 5 R->W 7First 12 item ids: ['gsm8k_train_7354', 'gsm8k_train_7354', 'gsm8k_train_4960', 'gsm8k_train_4960', 'gsm8k_train_4335', 'gsm8k_train_4335', 'gsm8k_train_3553', 'gsm8k_train_3553', 'gsm8k_train_5789', 'gsm8k_train_5789', 'gsm8k_train_1381', 'gsm8k_train_1381']

python

# Hypothesis: the four-neuron ablation did not repair the loop, so the next causal test should follow the latest measured state rather than activation correlations alone.import os, json, collectionsfrom pathlib import Pathimport numpy as npimport torchprint("Work files:", sorted(os.listdir("/work")))print("Notes:\n", Path("/work/notes.md").read_text() if Path("/work/notes.md").exists() else "none")print("Edits:", edited())for p in sorted(Path("/work").glob("fwd_*/meta.json")):    m=json.load(open(p))    print(p.parent.name, "keys",list(m), "lengths",m.get("lengths"), "model",m.get("model"))print("Available analysis variables:", sorted(k for k in globals() if not k.startswith("_") and k not in ["NAMES","tok"])[:180])

# Hypothesis: the four-neuron ablation did not repair the loop, so the next causal test should follow the latest measured state rather than activation correlations alone.import os, json, collectionsfrom pathlib import Pathimport numpy as npimport torchprint("Work files:", sorted(os.listdir("/work")))print("Notes:\n", Path("/work/notes.md").read_text() if Path("/work/notes.md").exists() else "none")print("Edits:", edited())for p in sorted(Path("/work").glob("fwd_*/meta.json")):    m=json.load(open(p))    print(p.parent.name, "keys",list(m), "lengths",m.get("lengths"), "model",m.get("model"))print("Available analysis variables:", sorted(k for k in globals() if not k.startswith("_") and k not in ["NAMES","tok"])[:180])

OUTPUT of Python run 34 of at most 300:

Work files: ['edited', 'fwd_3', 'gen_1.jsonl', 'gen_1_acts', 'gen_4_edited.jsonl', 'gen_4_edited_acts', 'gen_4_original.jsonl', 'gen_4_original_acts', 'notes.md']Notes: # Math weight-edit experimentBaseline pass 1: 256 original generations (96 GSM8K,160 MATH), full 32768-token limit.Accuracy 24/256=9.375%; 232 cutoffs (91.41%). Correct GSM=12/96, MATH=12/160.Failures enter exact repetitive thinking; 213/232 failed tails contain 'Wait,'.Repeated 12-token fractions first2048/middle/tail: failures .070/.506/.975, correct .039/.098/.177.Pass 2 (files fwd_3 after one uncounted attention-instrumentation error): two teacher-forced arithmetic/trigonometric probes, matched identical sentences at first vs repeated occurrences.Most consistent within-content repetition-associated MLP neurons:23:22 (+3.26),40 (-4.19),46 (+1.80),94 (+2.18),19 (+1.97);19:11 (-1.57);18:8 (-1.51);22:59 (+1.13).These signs agree across five distinct repetitive phrases and with failed-vs-correct generation averages.Caveat: teacher-forced chat template inserted empty thinking before supplied think; use raw templated text for follow-up.No edits tested yet. Eight forward passes remain. Need causal dose tests and independent paired validation.
Edits: ['model.language_model.layers.18.mlp.down_proj.weight', 'model.language_model.layers.19.mlp.down_proj.weight', 'model.language_model.layers.23.mlp.down_proj.weight']fwd_3 keys ['texts', 'lengths', 'model', 'edited_tensors', 'files', 'padding'] lengths [679, 613] model originalAvailable analysis variables: ['E', 'FROZEN', 'M1', 'MODEL', 'Path', 'Tokenizer', 'W', 'WORK', 'a', 'apply_circuit_strength', 'b', 'b4', 'by_id', 'candidates', 'changed', 'changed_cols', 'collections', 'cols', 'd', 'd4', 'data', 'dd', 'delta', 'di', 'diag_ids', 'direction', 'down_cache', 'drop_edit', 'e4', 'edit4', 'edited', 'factors', 'file', 'filename', 'flat', 'flat1', 'flatten_file', 'flatten_generations', 'fn', 'g4e', 'g4o', 'ge4', 'gen1', 'gen_4_edited', 'gen_4_original', 'go4', 'grouped', 'i', 'ids_of', 'idx1', 'inds', 'inspect', 'ix', 'j', 'js', 'json', 'key', 'l', 'label', 'load', 'load_original', 'm', 'math', 'mean_bad', 'mean_good', 'name', 'new', 'nf', 'notes', 'notes_path', 'np', 'o', 'obj', 'old', 'order', 'orig4', 'os', 'p', 'paired_summary', 'path', 'practice', 'r', 'ratio', 'read_flat', 'read_generations', 'records', 'repeated_fraction', 'rows', 'rs', 'safe_ids', 'safe_labels', 'safe_open', 'safe_words', 'save_edit', 'scores', 'src', 'ss', 'tag', 'targets', 'torch', 'v', 'value', 'var', 'w', 'w0', 'y1']

Work files: ['edited', 'fwd_3', 'gen_1.jsonl', 'gen_1_acts', 'gen_4_edited.jsonl', 'gen_4_edited_acts', 'gen_4_original.jsonl', 'gen_4_original_acts', 'notes.md']Notes: # Math weight-edit experimentBaseline pass 1: 256 original generations (96 GSM8K,160 MATH), full 32768-token limit.Accuracy 24/256=9.375%; 232 cutoffs (91.41%). Correct GSM=12/96, MATH=12/160.Failures enter exact repetitive thinking; 213/232 failed tails contain 'Wait,'.Repeated 12-token fractions first2048/middle/tail: failures .070/.506/.975, correct .039/.098/.177.Pass 2 (files fwd_3 after one uncounted attention-instrumentation error): two teacher-forced arithmetic/trigonometric probes, matched identical sentences at first vs repeated occurrences.Most consistent within-content repetition-associated MLP neurons:23:22 (+3.26),40 (-4.19),46 (+1.80),94 (+2.18),19 (+1.97);19:11 (-1.57);18:8 (-1.51);22:59 (+1.13).These signs agree across five distinct repetitive phrases and with failed-vs-correct generation averages.Caveat: teacher-forced chat template inserted empty thinking before supplied think; use raw templated text for follow-up.No edits tested yet. Eight forward passes remain. Need causal dose tests and independent paired validation.
Edits: ['model.language_model.layers.18.mlp.down_proj.weight', 'model.language_model.layers.19.mlp.down_proj.weight', 'model.language_model.layers.23.mlp.down_proj.weight']fwd_3 keys ['texts', 'lengths', 'model', 'edited_tensors', 'files', 'padding'] lengths [679, 613] model originalAvailable analysis variables: ['E', 'FROZEN', 'M1', 'MODEL', 'Path', 'Tokenizer', 'W', 'WORK', 'a', 'apply_circuit_strength', 'b', 'b4', 'by_id', 'candidates', 'changed', 'changed_cols', 'collections', 'cols', 'd', 'd4', 'data', 'dd', 'delta', 'di', 'diag_ids', 'direction', 'down_cache', 'drop_edit', 'e4', 'edit4', 'edited', 'factors', 'file', 'filename', 'flat', 'flat1', 'flatten_file', 'flatten_generations', 'fn', 'g4e', 'g4o', 'ge4', 'gen1', 'gen_4_edited', 'gen_4_original', 'go4', 'grouped', 'i', 'ids_of', 'idx1', 'inds', 'inspect', 'ix', 'j', 'js', 'json', 'key', 'l', 'label', 'load', 'load_original', 'm', 'math', 'mean_bad', 'mean_good', 'name', 'new', 'nf', 'notes', 'notes_path', 'np', 'o', 'obj', 'old', 'order', 'orig4', 'os', 'p', 'paired_summary', 'path', 'practice', 'r', 'ratio', 'read_flat', 'read_generations', 'records', 'repeated_fraction', 'rows', 'rs', 'safe_ids', 'safe_labels', 'safe_open', 'safe_words', 'save_edit', 'scores', 'src', 'ss', 'tag', 'targets', 'torch', 'v', 'value', 'var', 'w', 'w0', 'y1']

python

# Hypothesis: the existing three-layer edit causally weakens repetition; recover its exact strength and paired outcome before choosing a dose-response test.def read_flat_file(path):    out=[]    for line in open(path):        row=json.loads(line)        for s in row.get("samples",[]):            out.append(dict(id=row["id"],source=row.get("source",""),**s))    return outg4o=read_flat_file("/work/gen_4_original.jsonl")g4e=read_flat_file("/work/gen_4_edited.jsonl")print("Pass-4 sample counts:",len(g4o),len(g4e))def brief_pair(original,modified):    a=np.array([r["correct"] for r in original],float)    b=np.array([r["correct"] for r in modified],float)    d=b-a    return dict(n=len(a),original=float(a.mean()),edited=float(b.mean()),                gain=float(d.mean()),paired_se=float(d.std(ddof=1)/np.sqrt(len(d))),                wrong_to_right=int((d==1).sum()),right_to_wrong=int((d==-1).sum()),                original_cut=float(np.mean([r["cut"] for r in original])),                edited_cut=float(np.mean([r["cut"] for r in modified])),                original_tokens=[REDACTED] for r in original])),                edited_tokens=[REDACTED] for r in modified])))print("Paired result:",brief_pair(g4o,g4e))for source in sorted(set(r["source"] for r in g4o)):    print(source,brief_pair([r for r in g4o if r["source"]==source],[r for r in g4e if r["source"]==source]))for name in edited():    orig=load_original(name); new=load(name)    cols=torch.nonzero((orig!=new).any(dim=0)).flatten().tolist()    print(name,"changed columns",cols)    print("Column projection ratios:",[(j,round(float(torch.dot(orig[:,j],new[:,j])/orig[:,j].square().sum()),5)) for j in cols])print("Candidate specification:",str(globals().get("candidates"))[:800])print("Strength function:")try:    import inspect    print(inspect.getsource(apply_circuit_strength))except Exception as ex:    print(type(ex).__name__)

# Hypothesis: the existing three-layer edit causally weakens repetition; recover its exact strength and paired outcome before choosing a dose-response test.def read_flat_file(path):    out=[]    for line in open(path):        row=json.loads(line)        for s in row.get("samples",[]):            out.append(dict(id=row["id"],source=row.get("source",""),**s))    return outg4o=read_flat_file("/work/gen_4_original.jsonl")g4e=read_flat_file("/work/gen_4_edited.jsonl")print("Pass-4 sample counts:",len(g4o),len(g4e))def brief_pair(original,modified):    a=np.array([r["correct"] for r in original],float)    b=np.array([r["correct"] for r in modified],float)    d=b-a    return dict(n=len(a),original=float(a.mean()),edited=float(b.mean()),                gain=float(d.mean()),paired_se=float(d.std(ddof=1)/np.sqrt(len(d))),                wrong_to_right=int((d==1).sum()),right_to_wrong=int((d==-1).sum()),                original_cut=float(np.mean([r["cut"] for r in original])),                edited_cut=float(np.mean([r["cut"] for r in modified])),                original_tokens=[REDACTED] for r in original])),                edited_tokens=[REDACTED] for r in modified])))print("Paired result:",brief_pair(g4o,g4e))for source in sorted(set(r["source"] for r in g4o)):    print(source,brief_pair([r for r in g4o if r["source"]==source],[r for r in g4e if r["source"]==source]))for name in edited():    orig=load_original(name); new=load(name)    cols=torch.nonzero((orig!=new).any(dim=0)).flatten().tolist()    print(name,"changed columns",cols)    print("Column projection ratios:",[(j,round(float(torch.dot(orig[:,j],new[:,j])/orig[:,j].square().sum()),5)) for j in cols])print("Candidate specification:",str(globals().get("candidates"))[:800])print("Strength function:")try:    import inspect    print(inspect.getsource(apply_circuit_strength))except Exception as ex:    print(type(ex).__name__)

OUTPUT of Python run 35 of at most 300:

Pass-4 sample counts: 128 128Paired result: {'n': 128, 'original': 0.1015625, 'edited': 0.0859375, 'gain': -0.015625, 'paired_se': 0.02713423316130324, 'wrong_to_right': 5, 'right_to_wrong': 7, 'original_cut': 0.90625, 'edited_cut': 0.9140625, 'original_tokens': 30206.75, 'edited_tokens': 30362.765625}gsm8k {'n': 64, 'original': 0.125, 'edited': 0.109375, 'gain': -0.015625, 'paired_se': 0.04162013771115177, 'wrong_to_right': 3, 'right_to_wrong': 4, 'original_cut': 0.875, 'edited_cut': 0.890625, 'original_tokens': 29338.046875, 'edited_tokens': 29717.421875}math {'n': 64, 'original': 0.078125, 'edited': 0.0625, 'gain': -0.015625, 'paired_se': 0.03515969449616268, 'wrong_to_right': 2, 'right_to_wrong': 3, 'original_cut': 0.9375, 'edited_cut': 0.9375, 'original_tokens': 31075.453125, 'edited_tokens': 31008.109375}model.language_model.layers.18.mlp.down_proj.weight changed columns [8]Column projection ratios: [(8, 0.0)]model.language_model.layers.19.mlp.down_proj.weight changed columns [11]Column projection ratios: [(11, 0.0)]model.language_model.layers.23.mlp.down_proj.weight changed columns [22, 40]Column projection ratios: [(22, 0.0), (40, 0.0)]Candidate specification: {18: [8], 19: [11], 22: [59, 11, 89], 23: [22, 40, 46, 94, 19, 350]}Strength function:OSError

Pass-4 sample counts: 128 128Paired result: {'n': 128, 'original': 0.1015625, 'edited': 0.0859375, 'gain': -0.015625, 'paired_se': 0.02713423316130324, 'wrong_to_right': 5, 'right_to_wrong': 7, 'original_cut': 0.90625, 'edited_cut': 0.9140625, 'original_tokens': 30206.75, 'edited_tokens': 30362.765625}gsm8k {'n': 64, 'original': 0.125, 'edited': 0.109375, 'gain': -0.015625, 'paired_se': 0.04162013771115177, 'wrong_to_right': 3, 'right_to_wrong': 4, 'original_cut': 0.875, 'edited_cut': 0.890625, 'original_tokens': 29338.046875, 'edited_tokens': 29717.421875}math {'n': 64, 'original': 0.078125, 'edited': 0.0625, 'gain': -0.015625, 'paired_se': 0.03515969449616268, 'wrong_to_right': 2, 'right_to_wrong': 3, 'original_cut': 0.9375, 'edited_cut': 0.9375, 'original_tokens': 31075.453125, 'edited_tokens': 31008.109375}model.language_model.layers.18.mlp.down_proj.weight changed columns [8]Column projection ratios: [(8, 0.0)]model.language_model.layers.19.mlp.down_proj.weight changed columns [11]Column projection ratios: [(11, 0.0)]model.language_model.layers.23.mlp.down_proj.weight changed columns [22, 40]Column projection ratios: [(22, 0.0), (40, 0.0)]Candidate specification: {18: [8], 19: [11], 22: [59, 11, 89], 23: [22, 40, 46, 94, 19, 350]}Strength function:OSError

python

# Hypothesis: the four-neuron ablation did not causally repair repetition; recover experiment records before selecting a different circuit.print("Work entries:", sorted(os.listdir("/work")))if os.path.exists("/work/notes.md"):    print(Path("/work/notes.md").read_text()[:8500])print("Current edits:", edited())print("Relevant state variables:", [k for k in globals() if any(s in k.lower() for s in ["candidate","strength","paired","pass","spec","repeat","probe","score","head","dense"])][:100])

# Hypothesis: the four-neuron ablation did not causally repair repetition; recover experiment records before selecting a different circuit.print("Work entries:", sorted(os.listdir("/work")))if os.path.exists("/work/notes.md"):    print(Path("/work/notes.md").read_text()[:8500])print("Current edits:", edited())print("Relevant state variables:", [k for k in globals() if any(s in k.lower() for s in ["candidate","strength","paired","pass","spec","repeat","probe","score","head","dense"])][:100])

WHAT YOU HAVE.

A persistent Python kernel (no internet, no GPU): variables survive between your code blocks, like a notebook. The model's files are read-only in /model, practice problems read-only in /practice, and /work is your read-write folder. Available: numpy, scipy, scikit-learn, PyTorch (CPU), pandas, sympy, networkx, safetensors, tokenizers. Defined in advance: NAMES (all tensor names), load(name) (a float32 torch tensor: your current edit if there is one, else the original), load_original(name), save_edit(name, tensor), drop_edit(name), edited() and tok (the tokenizer). Gradients are disabled. To run code, reply with exactly one block that starts with ```python and nothing else; if its last line is an expression, its value is shown. One execution may take up to 900 seconds and 96 GB; output is cut at 10,000 characters. You may run code up to 300 times. If the kernel is restarted (time or memory limit), its variables are lost and you are told so.
Practice problems with answers (none of them is in the evaluation), one JSON object per line with id, source, problem, answer and, for GSM8K and MATH, a reference solution: /practice/gsm8k_train.jsonl (7,469 grade-school problems), /practice/math_train.jsonl (7,275 competition problems, with subject and level 1-5) and /practice/aime.jsonl (714 AIME problems, 1983-2023, answers only). Choose whichever serve your experiments.
A forward-pass tool with a budget of 10 forward passes in total. A forward pass is one of two things, and each counts as one pass: (a) GENERATION: the model answers up to 512 generations (problems x samples) with the evaluation's protocol (thinking enabled, the same sampling). Reply with exactly one block that starts with forward and contains JSON: {"type": "generate", "model": "original", "edited" or "both", "problems": ["<practice id>", ...], "prompts": ["<your own user message>", ...] (optional), "n": samples per item (items x n <= 512; with "both" <= 256 per model), "max_tokens": at most 32768, "activations": {"layers": [layer indices 0..24], "mlp": true or false} (optional)} "original" runs the unedited weights, "edited" your current edits, and "both" runs both on the same items and returns a paired comparison (the accuracy difference edited - original over items with its standard error, and how many samples went from wrong to right and back). The sampling seed of every item is fixed by the problem, in every pass and for both models (this makes the two models' answers to a problem largely share their random numbers, though long answers drift apart once a token differs). Every answer to a practice problem is graded. You get the accuracy (overall and by source), the mean length, the share cut at max_tokens and the failure counts; texts, extracted answers and grades are in /work/gen_<k>.jsonl (with "both": /work/gen_<k>_original.jsonl and /work/gen_<k>_edited.jsonl). With "activations", for every generated answer the model's residual stream at the requested layers (0 = embeddings, l = after layer l) is saved, taken at the last token of the prompt and averaged over the answer's first 8,192 tokens (and the MLPs' down_proj inputs with "mlp": true), with each answer's grade: /work/gen_<k>_acts/ (index.json lists the files and samples; with "both" one folder per model). (b) TEACHER-FORCED: the model reads up to 1,024 texts of up to 32,768 tokens each, at most 524,288 tokens in total (no generation; it takes seconds to a few minutes). Reply with a forward block containing JSON: {"type": "teacher_forced", "model": "original" or "edited", "texts": ["raw text", ...] or "chats": [[{"role": "user", "content": "..."}, {"role": "assistant", "content": "..."}], ...] (add "enable_thinking": true for the thinking template), "return": {"token_logprobs": true, "topk": k (at most 50), "hidden_states": [layers 0..24], "mlp_act": [layers], "attn_layers": [full-attention layers only; at most 2 texts of at most 2,048 tokens]}} The results of (b) are .npy files (with meta.json) in /work/fwd_/, right-padded to the longest text (at most 6 GB per pass). A request that fails does not count against the budget. A generation pass takes up to about half an hour at full size (answers can be 32,768 tokens long).

HOW TO WORK.

Diagnose first. Look at how the model fails on practice problems: a wrong final answer, finishing without an answer, or being cut off at the token limit (often while repeating itself in its thinking). The generation summary counts these; read some failing texts. Go after the largest category you can explain.
Localize causally. That a component is more active on correct answers is not enough (correct answers come from easier and shorter problems). Before relying on a component, show that changing it changes the behaviour you predicted, and try several strengths to see a dose-response.
Measure with enough power. Accuracy over n generations has a standard error of about 50/sqrt(n) percentage points: about 4.4 points at 128 generations, 2.2 at 512. Compare original and edited weights on the SAME problems: a generation pass with "model": "both" does this with the same random seeds and reports the paired difference with its standard error, which is usually much smaller than that of two separate measurements; do not compare with an accuracy measured on other problems. Keep an edit only if its gain is clearly larger than its standard error on problems that were not used to choose it. A lower share of cut-off answers does not by itself mean more correct answers: judge edits by accuracy. Dense measurements help too and are cheap: one teacher-forced pass can read the reference solutions of up to 1,024 practice problems (or the model's own good versus failing continuations) and gives a signal on every token. Edits that change a tensor by a fraction of a percent usually have effects too small to see; test strengths large enough to matter and back off if they hurt.
Plan your forward passes (see the budget above): for example one diagnostic generation with activations, teacher-forced passes for dense paired measurements, and a final paired generation ("model": "both") on problems you have not used to confirm.
Work efficiently: state the hypothesis of each code block in its first comment line; work through the model in parts; load things once into kernel variables; print compact summaries, never whole arrays (and never raw lists of vocabulary tokens: the vocabulary contains offensive and adult strings and printing them can get the session refused by the provider's content filter; print digits, math symbols, common math words or token ids instead; outputs are shown in ASCII only, other characters appear as '?'; likewise print short excerpts of generated texts, at most about 300 characters each, not whole transcripts); keep /work/notes.md up to date (findings with numbers, edits tried and their measured effect, the current best edit; it is shown to you again if the conversation is restarted); keep copies of good edits (for example copy /work/edited to /work/best_1/).

THIS IS A RESTARTED CONVERSATION (to keep it short): 40 replies happened before. Forward passes used: 3 of 10; Python runs used: 36 of 300. Your kernel is still running with its variables. Tensors currently edited: ['model.language_model.layers.18.mlp.down_proj.weight', 'model.language_model.layers.19.mlp.down_proj.weight', 'model.language_model.layers.23.mlp.down_proj.weight']. Files in /work: ['edited', 'fwd_3', 'gen_1.jsonl', 'gen_1_acts', 'gen_4_edited.jsonl', 'gen_4_edited_acts', 'gen_4_original.jsonl', 'gen_4_original_acts', 'notes.md']. YOUR NOTEBOOK (/work/notes.md, last 8,000 characters):

# Math weight-edit experimentBaseline pass 1: 256 original generations (96 GSM8K,160 MATH), full 32768-token limit.Accuracy 24/256=9.375%; 232 cutoffs (91.41%). Correct GSM=12/96, MATH=12/160.Failures enter exact repetitive thinking; 213/232 failed tails contain 'Wait,'.Repeated 12-token fractions first2048/middle/tail: failures .070/.506/.975, correct .039/.098/.177.Pass 2 (files fwd_3 after one uncounted attention-instrumentation error): two teacher-forced arithmetic/trigonometric probes, matched identical sentences at first vs repeated occurrences.Most consistent within-content repetition-associated MLP neurons:23:22 (+3.26),40 (-4.19),46 (+1.80),94 (+2.18),19 (+1.97);19:11 (-1.57);18:8 (-1.51);22:59 (+1.13).These signs agree across five distinct repetitive phrases and with failed-vs-correct generation averages.Caveat: teacher-forced chat template inserted empty thinking before supplied think; use raw templated text for follow-up.No edits tested yet. Eight forward passes remain. Need causal dose tests and independent paired validation.

# Math weight-edit experimentBaseline pass 1: 256 original generations (96 GSM8K,160 MATH), full 32768-token limit.Accuracy 24/256=9.375%; 232 cutoffs (91.41%). Correct GSM=12/96, MATH=12/160.Failures enter exact repetitive thinking; 213/232 failed tails contain 'Wait,'.Repeated 12-token fractions first2048/middle/tail: failures .070/.506/.975, correct .039/.098/.177.Pass 2 (files fwd_3 after one uncounted attention-instrumentation error): two teacher-forced arithmetic/trigonometric probes, matched identical sentences at first vs repeated occurrences.Most consistent within-content repetition-associated MLP neurons:23:22 (+3.26),40 (-4.19),46 (+1.80),94 (+2.18),19 (+1.97);19:11 (-1.57);18:8 (-1.51);22:59 (+1.13).These signs agree across five distinct repetitive phrases and with failed-vs-correct generation averages.Caveat: teacher-forced chat template inserted empty thinking before supplied think; use raw templated text for follow-up.No edits tested yet. Eight forward passes remain. Need causal dose tests and independent paired validation.

Continue from here.

Last result: OUTPUT of Python run 36 of at most 300:

Work entries: ['edited', 'fwd_3', 'gen_1.jsonl', 'gen_1_acts', 'gen_4_edited.jsonl', 'gen_4_edited_acts', 'gen_4_original.jsonl', 'gen_4_original_acts', 'notes.md']# Math weight-edit experimentBaseline pass 1: 256 original generations (96 GSM8K,160 MATH), full 32768-token limit.Accuracy 24/256=9.375%; 232 cutoffs (91.41%). Correct GSM=12/96, MATH=12/160.Failures enter exact repetitive thinking; 213/232 failed tails contain 'Wait,'.Repeated 12-token fractions first2048/middle/tail: failures .070/.506/.975, correct .039/.098/.177.Pass 2 (files fwd_3 after one uncounted attention-instrumentation error): two teacher-forced arithmetic/trigonometric probes, matched identical sentences at first vs repeated occurrences.Most consistent within-content repetition-associated MLP neurons:23:22 (+3.26),40 (-4.19),46 (+1.80),94 (+2.18),19 (+1.97);19:11 (-1.57);18:8 (-1.51);22:59 (+1.13).These signs agree across five distinct repetitive phrases and with failed-vs-correct generation averages.Caveat: teacher-forced chat template inserted empty thinking before supplied think; use raw templated text for follow-up.No edits tested yet. Eight forward passes remain. Need causal dose tests and independent paired validation.
Current edits: ['model.language_model.layers.18.mlp.down_proj.weight', 'model.language_model.layers.19.mlp.down_proj.weight', 'model.language_model.layers.23.mlp.down_proj.weight']Relevant state variables: ['candidates', 'scores', 'apply_circuit_strength', 'repeated_fraction', 'inspect', 'paired_summary']

Work entries: ['edited', 'fwd_3', 'gen_1.jsonl', 'gen_1_acts', 'gen_4_edited.jsonl', 'gen_4_edited_acts', 'gen_4_original.jsonl', 'gen_4_original_acts', 'notes.md']# Math weight-edit experimentBaseline pass 1: 256 original generations (96 GSM8K,160 MATH), full 32768-token limit.Accuracy 24/256=9.375%; 232 cutoffs (91.41%). Correct GSM=12/96, MATH=12/160.Failures enter exact repetitive thinking; 213/232 failed tails contain 'Wait,'.Repeated 12-token fractions first2048/middle/tail: failures .070/.506/.975, correct .039/.098/.177.Pass 2 (files fwd_3 after one uncounted attention-instrumentation error): two teacher-forced arithmetic/trigonometric probes, matched identical sentences at first vs repeated occurrences.Most consistent within-content repetition-associated MLP neurons:23:22 (+3.26),40 (-4.19),46 (+1.80),94 (+2.18),19 (+1.97);19:11 (-1.57);18:8 (-1.51);22:59 (+1.13).These signs agree across five distinct repetitive phrases and with failed-vs-correct generation averages.Caveat: teacher-forced chat template inserted empty thinking before supplied think; use raw templated text for follow-up.No edits tested yet. Eight forward passes remain. Need causal dose tests and independent paired validation.
Current edits: ['model.language_model.layers.18.mlp.down_proj.weight', 'model.language_model.layers.19.mlp.down_proj.weight', 'model.language_model.layers.23.mlp.down_proj.weight']Relevant state variables: ['candidates', 'scores', 'apply_circuit_strength', 'repeated_fraction', 'inspect', 'paired_summary']

python

# Hypothesis: the existing circuit edit should reduce repetitive thinking; recover its strength and paired outcomes before choosing the next dose.import os, json, inspect, numpy as np, torchprint("Analysis variables:", sorted(k for k in globals() if not k.startswith("_") and any(s in k.lower() for s in ["strength", "candidate", "score", "circuit", "pair", "edit", "probe", "select"])))for fn in ["apply_circuit_strength", "paired_summary", "repeated_fraction"]:    obj = globals().get(fn)    if obj is not None:        try:            print(fn, inspect.getsource(obj))        except Exception:            print(fn, "signature:", inspect.signature(obj), "constants:", obj.__code__.co_consts)for path in ["/work/gen_4_original.jsonl", "/work/gen_4_edited.jsonl"]:    with open(path) as f:        rows = [json.loads(line) for line in f]    print(os.path.basename(path), "count:", len(rows), "keys:", list(rows[0]))    print("First record metadata:", {k:v for k,v in rows[0].items() if not isinstance(v, (dict,list)) and not (isinstance(v,str) and len(v)>100)})print("Candidates:", repr(globals().get("candidates"))[:1800])for name in edited():    w, w0 = load(name), load_original(name)    changed = (w != w0).any(dim=0) if w.ndim == 2 else (w != w0)    print(name, "shape:", tuple(w.shape), "changed columns:", int(changed.sum()), "relative delta:", float((w-w0).norm()/w0.norm()))

# Hypothesis: the existing circuit edit should reduce repetitive thinking; recover its strength and paired outcomes before choosing the next dose.import os, json, inspect, numpy as np, torchprint("Analysis variables:", sorted(k for k in globals() if not k.startswith("_") and any(s in k.lower() for s in ["strength", "candidate", "score", "circuit", "pair", "edit", "probe", "select"])))for fn in ["apply_circuit_strength", "paired_summary", "repeated_fraction"]:    obj = globals().get(fn)    if obj is not None:        try:            print(fn, inspect.getsource(obj))        except Exception:            print(fn, "signature:", inspect.signature(obj), "constants:", obj.__code__.co_consts)for path in ["/work/gen_4_original.jsonl", "/work/gen_4_edited.jsonl"]:    with open(path) as f:        rows = [json.loads(line) for line in f]    print(os.path.basename(path), "count:", len(rows), "keys:", list(rows[0]))    print("First record metadata:", {k:v for k,v in rows[0].items() if not isinstance(v, (dict,list)) and not (isinstance(v,str) and len(v)>100)})print("Candidates:", repr(globals().get("candidates"))[:1800])for name in edited():    w, w0 = load(name), load_original(name)    changed = (w != w0).any(dim=0) if w.ndim == 2 else (w != w0)    print(name, "shape:", tuple(w.shape), "changed columns:", int(changed.sum()), "relative delta:", float((w-w0).norm()/w0.norm()))

OUTPUT of Python run 37 of at most 300:

Analysis variables: ['apply_circuit_strength', 'brief_pair', 'candidates', 'drop_edit', 'edit4', 'edited', 'gen_4_edited', 'paired_summary', 'save_edit', 'scores']apply_circuit_strength signature: (strength) constants: (None, 'model.language_model.layers.', '.mlp.down_proj.weight', 1)paired_summary signature: (a, b) constants: (None, <code object <listcomp> at 0x7f4929abe970, file "<run>", line 12>, 'paired_summary.<locals>.<listcomp>', <code object <listcomp> at 0x7f4929abe8c0, file "<run>", line 13>, 1, ('ddof',), 0, <code object <listcomp> at 0x7f4929abec30, file "<run>", line 18>, <code object <listcomp> at 0x7f4929abed90, file "<run>", line 19>, <code object <listcomp> at 0x7f4929abece0, file "<run>", line 20>, <code object <listcomp> at 0x7f4929abf050, file "<run>", line 21>, ('n', 'original_correct', 'edited_correct', 'gain', 'paired_se', 'rescued', 'regressed', 'original_cut', 'edited_cut', 'original_mean_tokens', 'edited_mean_tokens'))repeated_fraction signature: (ids, n=12) constants: (None, 0.0, <code object <listcomp> at 0x7f4929a726b0, file "<run>", line 15>, 'repeated_fraction.<locals>.<listcomp>', 1)gen_4_original.jsonl count: 64 keys: ['id', 'source', 'answer', 'samples']First record metadata: {'id': 'gsm8k_train_7354', 'source': 'gsm8k', 'answer': '12'}gen_4_edited.jsonl count: 64 keys: ['id', 'source', 'answer', 'samples']First record metadata: {'id': 'gsm8k_train_7354', 'source': 'gsm8k', 'answer': '12'}Candidates: {18: [8], 19: [11], 22: [59, 11, 89], 23: [22, 40, 46, 94, 19, 350]}model.language_model.layers.18.mlp.down_proj.weight shape: (1024, 3584) changed columns: 1 relative delta: 0.017899204045534134model.language_model.layers.19.mlp.down_proj.weight shape: (1024, 3584) changed columns: 1 relative delta: 0.015992557629942894model.language_model.layers.23.mlp.down_proj.weight shape: (1024, 3584) changed columns: 2 relative delta: 0.019595080986618996

Analysis variables: ['apply_circuit_strength', 'brief_pair', 'candidates', 'drop_edit', 'edit4', 'edited', 'gen_4_edited', 'paired_summary', 'save_edit', 'scores']apply_circuit_strength signature: (strength) constants: (None, 'model.language_model.layers.', '.mlp.down_proj.weight', 1)paired_summary signature: (a, b) constants: (None, <code object <listcomp> at 0x7f4929abe970, file "<run>", line 12>, 'paired_summary.<locals>.<listcomp>', <code object <listcomp> at 0x7f4929abe8c0, file "<run>", line 13>, 1, ('ddof',), 0, <code object <listcomp> at 0x7f4929abec30, file "<run>", line 18>, <code object <listcomp> at 0x7f4929abed90, file "<run>", line 19>, <code object <listcomp> at 0x7f4929abece0, file "<run>", line 20>, <code object <listcomp> at 0x7f4929abf050, file "<run>", line 21>, ('n', 'original_correct', 'edited_correct', 'gain', 'paired_se', 'rescued', 'regressed', 'original_cut', 'edited_cut', 'original_mean_tokens', 'edited_mean_tokens'))repeated_fraction signature: (ids, n=12) constants: (None, 0.0, <code object <listcomp> at 0x7f4929a726b0, file "<run>", line 15>, 'repeated_fraction.<locals>.<listcomp>', 1)gen_4_original.jsonl count: 64 keys: ['id', 'source', 'answer', 'samples']First record metadata: {'id': 'gsm8k_train_7354', 'source': 'gsm8k', 'answer': '12'}gen_4_edited.jsonl count: 64 keys: ['id', 'source', 'answer', 'samples']First record metadata: {'id': 'gsm8k_train_7354', 'source': 'gsm8k', 'answer': '12'}Candidates: {18: [8], 19: [11], 22: [59, 11, 89], 23: [22, 40, 46, 94, 19, 350]}model.language_model.layers.18.mlp.down_proj.weight shape: (1024, 3584) changed columns: 1 relative delta: 0.017899204045534134model.language_model.layers.19.mlp.down_proj.weight shape: (1024, 3584) changed columns: 1 relative delta: 0.015992557629942894model.language_model.layers.23.mlp.down_proj.weight shape: (1024, 3584) changed columns: 2 relative delta: 0.019595080986618996

python

# Hypothesis: a few repetition-linked MLP channels, rather than broad rescaling, explain the first intervention's effect.import disdis.dis(apply_circuit_strength)print("Circuit globals:", {k:globals().get(k) for k in apply_circuit_strength.__code__.co_names if k in globals() and isinstance(globals()[k],(int,float,str,list,dict,tuple))})a4 = [json.loads(s) for s in open("/work/gen_4_original.jsonl")]b4 = [json.loads(s) for s in open("/work/gen_4_edited.jsonl")]print("Sample keys:", list(a4[0]["samples"][0]))print("Original sample metadata:", {k:v for k,v in a4[0]["samples"][0].items() if not (isinstance(v,str) and len(v)>120) and not isinstance(v,(list,dict))})print("Edited sample metadata:", {k:v for k,v in b4[0]["samples"][0].items() if not (isinstance(v,str) and len(v)>120) and not isinstance(v,(list,dict))})try:    print("Paired summary:", paired_summary(a4,b4))except Exception as e:    print(type(e).__name__, str(e))for name in edited():    w,w0 = load(name),load_original(name)    cols = torch.where((w != w0).any(dim=0))[0]    print(name, [(int(j), round(float(torch.dot(w[:,j],w0[:,j])/w0[:,j].square().sum()),4)) for j in cols])print("Other globals:", sorted(k for k in globals() if not k.startswith("_"))[:180])

# Hypothesis: a few repetition-linked MLP channels, rather than broad rescaling, explain the first intervention's effect.import disdis.dis(apply_circuit_strength)print("Circuit globals:", {k:globals().get(k) for k in apply_circuit_strength.__code__.co_names if k in globals() and isinstance(globals()[k],(int,float,str,list,dict,tuple))})a4 = [json.loads(s) for s in open("/work/gen_4_original.jsonl")]b4 = [json.loads(s) for s in open("/work/gen_4_edited.jsonl")]print("Sample keys:", list(a4[0]["samples"][0]))print("Original sample metadata:", {k:v for k,v in a4[0]["samples"][0].items() if not (isinstance(v,str) and len(v)>120) and not isinstance(v,(list,dict))})print("Edited sample metadata:", {k:v for k,v in b4[0]["samples"][0].items() if not (isinstance(v,str) and len(v)>120) and not isinstance(v,(list,dict))})try:    print("Paired summary:", paired_summary(a4,b4))except Exception as e:    print(type(e).__name__, str(e))for name in edited():    w,w0 = load(name),load_original(name)    cols = torch.where((w != w0).any(dim=0))[0]    print(name, [(int(j), round(float(torch.dot(w[:,j],w0[:,j])/w0[:,j].square().sum()),4)) for j in cols])print("Other globals:", sorted(k for k in globals() if not k.startswith("_"))[:180])

OUTPUT of Python run 38 of at most 300:

  5           0 LOAD_GLOBAL              0 (list)              2 LOAD_GLOBAL              1 (edited)              4 CALL_FUNCTION            0              6 CALL_FUNCTION            1              8 GET_ITER        >>   10 FOR_ITER                 6 (to 24)             12 STORE_FAST               1 (name)
  6          14 LOAD_GLOBAL              2 (drop_edit)             16 LOAD_FAST                1 (name)             18 CALL_FUNCTION            1             20 POP_TOP             22 JUMP_ABSOLUTE            5 (to 10)
  7     >>   24 BUILD_LIST               0             26 STORE_FAST               2 (changes)
  8          28 LOAD_GLOBAL              3 (targets)             30 LOAD_METHOD              4 (items)             32 CALL_METHOD              0             34 GET_ITER        >>   36 FOR_ITER                55 (to 148)             38 UNPACK_SEQUENCE          2             40 STORE_FAST               3 (l)             42 STORE_FAST               4 (js)
  9          44 LOAD_CONST               1 ('model.language_model.layers.')             46 LOAD_FAST                3 (l)             48 FORMAT_VALUE             0             50 LOAD_CONST               2 ('.mlp.down_proj.weight')             52 BUILD_STRING             3             54 STORE_FAST               1 (name)
 10          56 LOAD_GLOBAL              5 (load_original)             58 LOAD_FAST                1 (name)             60 CALL_FUNCTION            1             62 STORE_FAST               5 (w)
 11          64 LOAD_FAST                5 (w)             66 LOAD_METHOD              6 (clone)             68 CALL_METHOD              0             70 STORE_FAST               6 (new)
 12          72 LOAD_FAST                6 (new)             74 LOAD_CONST               0 (None)             76 LOAD_CONST               0 (None)             78 BUILD_SLICE              2             80 LOAD_FAST                4 (js)             82 BUILD_TUPLE              2             84 DUP_TOP_TWO             86 BINARY_SUBSCR             88 LOAD_CONST               3 (1)             90 LOAD_FAST                0 (strength)             92 BINARY_SUBTRACT             94 INPLACE_MULTIPLY             96 ROT_THREE             98 STORE_SUBSCR
 13         100 LOAD_GLOBAL              7 (save_edit)            102 LOAD_FAST                1 (name)            104 LOAD_FAST                6 (new)            106 CALL_FUNCTION            2            108 POP_TOP
 14         110 LOAD_FAST                2 (changes)            112 LOAD_METHOD              8 (append)            114 LOAD_FAST                3 (l)            116 LOAD_FAST                4 (js)            118 LOAD_GLOBAL              9 (float)            120 LOAD_FAST                6 (new)            122 LOAD_FAST                5 (w)            124 BINARY_SUBTRACT            126 LOAD_METHOD             10 (norm)            128 CALL_METHOD              0            130 LOAD_FAST                5 (w)            132 LOAD_METHOD             10 (norm)            134 CALL_METHOD              0            136 BINARY_TRUE_DIVIDE            138 CALL_FUNCTION            1            140 BUILD_TUPLE              3            142 CALL_METHOD              1            144 POP_TOP            146 JUMP_ABSOLUTE           18 (to 36)
 15     >>  148 LOAD_FAST                2 (changes)            150 RETURN_VALUECircuit globals: {'targets': {18: [8], 19: [11], 23: [22, 40]}}Sample keys: ['text', 'pred', 'correct', 'tokens', 'cut']Original sample metadata: {'pred': None, 'correct': False, 'tokens': 32768, 'cut': True}Edited sample metadata: {'pred': None, 'correct': False, 'tokens': 32768, 'cut': True}KeyError 'correct'model.language_model.layers.18.mlp.down_proj.weight [(8, 0.0)]model.language_model.layers.19.mlp.down_proj.weight [(11, 0.0)]model.language_model.layers.23.mlp.down_proj.weight [(22, 0.0), (40, 0.0)]Other globals: ['E', 'FROZEN', 'M1', 'MODEL', 'NAMES', 'Path', 'Tokenizer', 'W', 'WORK', 'a', 'a4', 'apply_circuit_strength', 'b', 'b4', 'brief_pair', 'by_id', 'candidates', 'changed', 'changed_cols', 'collections', 'cols', 'd', 'd4', 'data', 'dd', 'delta', 'di', 'diag_ids', 'direction', 'dis', 'down_cache', 'drop_edit', 'e4', 'edit4', 'edited', 'f', 'factors', 'file', 'filename', 'flat', 'flat1', 'flatten_file', 'flatten_generations', 'fn', 'g4e', 'g4o', 'ge4', 'gen1', 'gen_4_edited', 'gen_4_original', 'go4', 'grouped', 'i', 'ids_of', 'idx1', 'inds', 'inspect', 'ix', 'j', 'js', 'json', 'key', 'l', 'label', 'load', 'load_original', 'm', 'math', 'mean_bad', 'mean_good', 'name', 'new', 'nf', 'notes', 'notes_path', 'np', 'o', 'obj', 'old', 'order', 'orig', 'orig4', 'os', 'p', 'paired_summary', 'path', 'practice', 'r', 'ratio', 'read_flat', 'read_flat_file', 'read_generations', 'records', 'repeated_fraction', 'rows', 'rs', 'safe_ids', 'safe_labels', 'safe_open', 'safe_words', 'save_edit', 'scores', 'source', 'src', 'ss', 'tag', 'targets', 'tok', 'torch', 'v', 'value', 'var', 'w', 'w0', 'y1']

  5           0 LOAD_GLOBAL              0 (list)              2 LOAD_GLOBAL              1 (edited)              4 CALL_FUNCTION            0              6 CALL_FUNCTION            1              8 GET_ITER        >>   10 FOR_ITER                 6 (to 24)             12 STORE_FAST               1 (name)
  6          14 LOAD_GLOBAL              2 (drop_edit)             16 LOAD_FAST                1 (name)             18 CALL_FUNCTION            1             20 POP_TOP             22 JUMP_ABSOLUTE            5 (to 10)
  7     >>   24 BUILD_LIST               0             26 STORE_FAST               2 (changes)
  8          28 LOAD_GLOBAL              3 (targets)             30 LOAD_METHOD              4 (items)             32 CALL_METHOD              0             34 GET_ITER        >>   36 FOR_ITER                55 (to 148)             38 UNPACK_SEQUENCE          2             40 STORE_FAST               3 (l)             42 STORE_FAST               4 (js)
  9          44 LOAD_CONST               1 ('model.language_model.layers.')             46 LOAD_FAST                3 (l)             48 FORMAT_VALUE             0             50 LOAD_CONST               2 ('.mlp.down_proj.weight')             52 BUILD_STRING             3             54 STORE_FAST               1 (name)
 10          56 LOAD_GLOBAL              5 (load_original)             58 LOAD_FAST                1 (name)             60 CALL_FUNCTION            1             62 STORE_FAST               5 (w)
 11          64 LOAD_FAST                5 (w)             66 LOAD_METHOD              6 (clone)             68 CALL_METHOD              0             70 STORE_FAST               6 (new)
 12          72 LOAD_FAST                6 (new)             74 LOAD_CONST               0 (None)             76 LOAD_CONST               0 (None)             78 BUILD_SLICE              2             80 LOAD_FAST                4 (js)             82 BUILD_TUPLE              2             84 DUP_TOP_TWO             86 BINARY_SUBSCR             88 LOAD_CONST               3 (1)             90 LOAD_FAST                0 (strength)             92 BINARY_SUBTRACT             94 INPLACE_MULTIPLY             96 ROT_THREE             98 STORE_SUBSCR
 13         100 LOAD_GLOBAL              7 (save_edit)            102 LOAD_FAST                1 (name)            104 LOAD_FAST                6 (new)            106 CALL_FUNCTION            2            108 POP_TOP
 14         110 LOAD_FAST                2 (changes)            112 LOAD_METHOD              8 (append)            114 LOAD_FAST                3 (l)            116 LOAD_FAST                4 (js)            118 LOAD_GLOBAL              9 (float)            120 LOAD_FAST                6 (new)            122 LOAD_FAST                5 (w)            124 BINARY_SUBTRACT            126 LOAD_METHOD             10 (norm)            128 CALL_METHOD              0            130 LOAD_FAST                5 (w)            132 LOAD_METHOD             10 (norm)            134 CALL_METHOD              0            136 BINARY_TRUE_DIVIDE            138 CALL_FUNCTION            1            140 BUILD_TUPLE              3            142 CALL_METHOD              1            144 POP_TOP            146 JUMP_ABSOLUTE           18 (to 36)
 15     >>  148 LOAD_FAST                2 (changes)            150 RETURN_VALUECircuit globals: {'targets': {18: [8], 19: [11], 23: [22, 40]}}Sample keys: ['text', 'pred', 'correct', 'tokens', 'cut']Original sample metadata: {'pred': None, 'correct': False, 'tokens': 32768, 'cut': True}Edited sample metadata: {'pred': None, 'correct': False, 'tokens': 32768, 'cut': True}KeyError 'correct'model.language_model.layers.18.mlp.down_proj.weight [(8, 0.0)]model.language_model.layers.19.mlp.down_proj.weight [(11, 0.0)]model.language_model.layers.23.mlp.down_proj.weight [(22, 0.0), (40, 0.0)]Other globals: ['E', 'FROZEN', 'M1', 'MODEL', 'NAMES', 'Path', 'Tokenizer', 'W', 'WORK', 'a', 'a4', 'apply_circuit_strength', 'b', 'b4', 'brief_pair', 'by_id', 'candidates', 'changed', 'changed_cols', 'collections', 'cols', 'd', 'd4', 'data', 'dd', 'delta', 'di', 'diag_ids', 'direction', 'dis', 'down_cache', 'drop_edit', 'e4', 'edit4', 'edited', 'f', 'factors', 'file', 'filename', 'flat', 'flat1', 'flatten_file', 'flatten_generations', 'fn', 'g4e', 'g4o', 'ge4', 'gen1', 'gen_4_edited', 'gen_4_original', 'go4', 'grouped', 'i', 'ids_of', 'idx1', 'inds', 'inspect', 'ix', 'j', 'js', 'json', 'key', 'l', 'label', 'load', 'load_original', 'm', 'math', 'mean_bad', 'mean_good', 'name', 'new', 'nf', 'notes', 'notes_path', 'np', 'o', 'obj', 'old', 'order', 'orig', 'orig4', 'os', 'p', 'paired_summary', 'path', 'practice', 'r', 'ratio', 'read_flat', 'read_flat_file', 'read_generations', 'records', 'repeated_fraction', 'rows', 'rs', 'safe_ids', 'safe_labels', 'safe_open', 'safe_words', 'save_edit', 'scores', 'source', 'src', 'ss', 'tag', 'targets', 'tok', 'torch', 'v', 'value', 'var', 'w', 'w0', 'y1']

python

# Hypothesis: deleting the four candidate channels causally interrupts loops and improves accuracy on the same sampled problems.s4o = [s for r in a4 for s in r["samples"]]s4e = [s for r in b4 for s in r["samples"]]summary4 = paired_summary(s4o,s4e)print("All:", summary4)for src in sorted({r["source"] for r in a4}):    print(src, paired_summary([s for r in a4 if r["source"]==src for s in r["samples"]],                              [s for r in b4 if r["source"]==src for s in r["samples"]]))for label,ss in [("original",s4o),("edit_strength_1",s4e)]:    frac = [repeated_fraction(tok.encode(s["text"]).ids[-2048:]) for s in ss]    print(label, "tail repetitive fraction mean/median:", np.round([np.mean(frac),np.median(frac)],4))for i,(ra,rb) in enumerate(zip(a4,b4)):    sa,sb = ra["samples"][0],rb["samples"][0]    if sa["correct"] != sb["correct"] or (sa["cut"] and not sb["cut"]):        print("Changed:", ra["id"], "correct",sa["correct"],"->",sb["correct"],"tokens",sa["tokens"],"->",sb["tokens"],              "edited tail:",sb["text"][-250:].encode("ascii","replace").decode())print("Activation index excerpt:",open("/work/gen_4_edited_acts/index.json").read()[:1800])with open("/work/notes.md","a") as f:    f.write("\nPass 3 (gen_4): ablated four down_proj columns: L18:8, L19:11, L23:22,40 (strength 1). Paired 64 problems. "+str(summary4)+"\n")

# Hypothesis: deleting the four candidate channels causally interrupts loops and improves accuracy on the same sampled problems.s4o = [s for r in a4 for s in r["samples"]]s4e = [s for r in b4 for s in r["samples"]]summary4 = paired_summary(s4o,s4e)print("All:", summary4)for src in sorted({r["source"] for r in a4}):    print(src, paired_summary([s for r in a4 if r["source"]==src for s in r["samples"]],                              [s for r in b4 if r["source"]==src for s in r["samples"]]))for label,ss in [("original",s4o),("edit_strength_1",s4e)]:    frac = [repeated_fraction(tok.encode(s["text"]).ids[-2048:]) for s in ss]    print(label, "tail repetitive fraction mean/median:", np.round([np.mean(frac),np.median(frac)],4))for i,(ra,rb) in enumerate(zip(a4,b4)):    sa,sb = ra["samples"][0],rb["samples"][0]    if sa["correct"] != sb["correct"] or (sa["cut"] and not sb["cut"]):        print("Changed:", ra["id"], "correct",sa["correct"],"->",sb["correct"],"tokens",sa["tokens"],"->",sb["tokens"],              "edited tail:",sb["text"][-250:].encode("ascii","replace").decode())print("Activation index excerpt:",open("/work/gen_4_edited_acts/index.json").read()[:1800])with open("/work/notes.md","a") as f:    f.write("\nPass 3 (gen_4): ablated four down_proj columns: L18:8, L19:11, L23:22,40 (strength 1). Paired 64 problems. "+str(summary4)+"\n")

OUTPUT of Python run 39 of at most 300:

Traceback (most recent call last):  File "<string>", line 17, in <module>  File "<run>", line 4, in <module>  File "<run>", line 12, in paired_summary  File "<run>", line 12, in <listcomp>KeyError: 'id'

Traceback (most recent call last):  File "<string>", line 17, in <module>  File "<run>", line 4, in <module>  File "<run>", line 12, in paired_summary  File "<run>", line 12, in <listcomp>KeyError: 'id'

python

# Hypothesis: paired outcomes, not lower repetition alone, determine whether the four-channel ablation is useful.def flat_records(rs):    return [dict(s,id=r["id"],source=r["source"],sample=j) for r in rs for j,s in enumerate(r["samples"])]s4o,s4e = flat_records(a4),flat_records(b4)def compare_samples(a,b):    assert [(s["id"],s["sample"]) for s in a] == [(s["id"],s["sample"]) for s in b]    x=np.array([s["correct"] for s in a],dtype=float)    y=np.array([s["correct"] for s in b],dtype=float)    d=y-x    return dict(n=len(a),original_correct=int(x.sum()),edited_correct=int(y.sum()),                gain_pp=round(100*d.mean(),3),paired_se_pp=round(100*d.std(ddof=1)/np.sqrt(len(d)),3),                rescued=int((d==1).sum()),regressed=int((d==-1).sum()),                original_cut=sum(s["cut"] for s in a),edited_cut=sum(s["cut"] for s in b),                original_mean_tokens=[REDACTED] for s in a])),                edited_mean_tokens=[REDACTED] for s in b])))summary4=compare_samples(s4o,s4e)print("All:",summary4)for src in sorted({s["source"] for s in s4o}):    print(src,compare_samples([s for s in s4o if s["source"]==src],[s for s in s4e if s["source"]==src]))for label,ss in [("original",s4o),("edit_strength_1",s4e)]:    frac=[repeated_fraction(tok.encode(s["text"]).ids[-2048:]) for s in ss]    print(label,"tail repetition mean/median:",np.round([np.mean(frac),np.median(frac)],4))changes=[(sa,sb) for sa,sb in zip(s4o,s4e) if sa["correct"]!=sb["correct"] or (sa["cut"] and not sb["cut"])]for sa,sb in changes[:8]:    print(sa["id"],"correct",sa["correct"],"->",sb["correct"],"tokens",sa["tokens"],"->",sb["tokens"],          "edited tail:",sb["text"][-200:].encode("ascii","replace").decode())print("Activation index:",open("/work/gen_4_edited_acts/index.json").read()[:1200])with open("/work/notes.md","a") as f:    f.write("\nPass 3 (gen_4): ablated down_proj columns L18:8, L19:11, L23:22,40, strength 1. "+str(summary4)+"\n")

# Hypothesis: paired outcomes, not lower repetition alone, determine whether the four-channel ablation is useful.def flat_records(rs):    return [dict(s,id=r["id"],source=r["source"],sample=j) for r in rs for j,s in enumerate(r["samples"])]s4o,s4e = flat_records(a4),flat_records(b4)def compare_samples(a,b):    assert [(s["id"],s["sample"]) for s in a] == [(s["id"],s["sample"]) for s in b]    x=np.array([s["correct"] for s in a],dtype=float)    y=np.array([s["correct"] for s in b],dtype=float)    d=y-x    return dict(n=len(a),original_correct=int(x.sum()),edited_correct=int(y.sum()),                gain_pp=round(100*d.mean(),3),paired_se_pp=round(100*d.std(ddof=1)/np.sqrt(len(d)),3),                rescued=int((d==1).sum()),regressed=int((d==-1).sum()),                original_cut=sum(s["cut"] for s in a),edited_cut=sum(s["cut"] for s in b),                original_mean_tokens=[REDACTED] for s in a])),                edited_mean_tokens=[REDACTED] for s in b])))summary4=compare_samples(s4o,s4e)print("All:",summary4)for src in sorted({s["source"] for s in s4o}):    print(src,compare_samples([s for s in s4o if s["source"]==src],[s for s in s4e if s["source"]==src]))for label,ss in [("original",s4o),("edit_strength_1",s4e)]:    frac=[repeated_fraction(tok.encode(s["text"]).ids[-2048:]) for s in ss]    print(label,"tail repetition mean/median:",np.round([np.mean(frac),np.median(frac)],4))changes=[(sa,sb) for sa,sb in zip(s4o,s4e) if sa["correct"]!=sb["correct"] or (sa["cut"] and not sb["cut"])]for sa,sb in changes[:8]:    print(sa["id"],"correct",sa["correct"],"->",sb["correct"],"tokens",sa["tokens"],"->",sb["tokens"],          "edited tail:",sb["text"][-200:].encode("ascii","replace").decode())print("Activation index:",open("/work/gen_4_edited_acts/index.json").read()[:1200])with open("/work/notes.md","a") as f:    f.write("\nPass 3 (gen_4): ablated down_proj columns L18:8, L19:11, L23:22,40, strength 1. "+str(summary4)+"\n")

OUTPUT of Python run 40 of at most 300:

All: {'n': 128, 'original_correct': 13, 'edited_correct': 11, 'gain_pp': np.float64(-1.562), 'paired_se_pp': np.float64(2.713), 'rescued': 5, 'regressed': 7, 'original_cut': 116, 'edited_cut': 117, 'original_mean_tokens': 30207, 'edited_mean_tokens': 30363}gsm8k {'n': 64, 'original_correct': 8, 'edited_correct': 7, 'gain_pp': np.float64(-1.562), 'paired_se_pp': np.float64(4.162), 'rescued': 3, 'regressed': 4, 'original_cut': 56, 'edited_cut': 57, 'original_mean_tokens': 29338, 'edited_mean_tokens': 29717}math {'n': 64, 'original_correct': 5, 'edited_correct': 4, 'gain_pp': np.float64(-1.562), 'paired_se_pp': np.float64(3.516), 'rescued': 2, 'regressed': 3, 'original_cut': 60, 'edited_cut': 60, 'original_mean_tokens': 31075, 'edited_mean_tokens': 31008}original tail repetition mean/median: [0.8585 0.9728]edit_strength_1 tail repetition mean/median: [0.8708 0.9762]gsm8k_train_4335 correct True -> True tokens 32768 -> 1358 edited tail: ch box contains 20 matches.*   Total matches = (Number of boxes) $\times$ (Matches per box)*   Total matches = $60 \times 20$.*   $60 \times 20 = 1200$.
Thus, James has 1200 matches.
\boxed{1200}gsm8k_train_7076 correct True -> False tokens 13476 -> 32768 edited tail: .    Wait, why did I think $Cost_C - Cost_O = 95$?    Wait, $135 - 40 = 95$.    Wait, why did I think $Cost_C - Cost_O = 95$?    Wait, $135 - 40 = 95$.    Wait, why did I think $Cost_C - Cost_O =gsm8k_train_462 correct False -> True tokens 32768 -> 7302 edited tail: of skips Valerie makes:$$ \text{Total Skips} = \text{Skips}_{\text{Roberto}} + \text{Skips}_{\text{Valerie}} $$$$ \text{Total Skips} = 1,050 + 1,200 $$$$ \text{Total Skips} = 2,250 $$
\boxed{2250}gsm8k_train_5989 correct False -> True tokens 32768 -> 9075 edited tail: hases equals \$7.*   $\text{Starting Money} - \text{Total Cost} = \$7$*   $\text{Starting Money} - \$46 = \$7$*   $\text{Starting Money} = \$46 + \$7$*   $\text{Starting Money} = \$53$
\boxed{53}gsm8k_train_1028 correct True -> False tokens 9531 -> 32768 edited tail: hat I did.            *   Wait, is it possible that "20 bars" refers to the number of bars in the container?            *   Yes, that's what I did.            *   Wait, is it possible that "20 barsgsm8k_train_714 correct True -> False tokens 5123 -> 32768 edited tail: per visit" means he gets $0.01 *per visit*?    *   If he gets $0.01 per visit, he gets $300.    *   Wait, what if "gets $0.01 per visit" means he gets $0.01 *per visit*?    *   If he gets $0.01 pergsm8k_train_350 correct True -> False tokens 837 -> 32768 edited tail: re rolls of wrapping paper does Chandler need to sell [for the total available]?"    *   No.    *   Let's check if the question is "How many more rolls of wrapping paper does Chandler need to sell [gsm8k_train_5000 correct False -> True tokens 32768 -> 2169 edited tail: times \$20.00 = \$2.00$$
**Step 3: Calculate the final total bill**
Subtract the discount from the subtotal:$$\$20.00 - \$2.00 = \$18.00$$
The total grocery bill comes to $\$18.00$.
\boxed{18.00}Activation index: {"layers": [18, 19, 23], "mlp_layers": [18, 19, 23], "samples": [{"item": "gsm8k_train_7354", "sample": 0, "correct": false, "tokens": 8192}, {"item": "gsm8k_train_7354", "sample": 1, "correct": false, "tokens": 8192}, {"item": "gsm8k_train_4960", "sample": 0, "correct": false, "tokens": 8192}, {"item": "gsm8k_train_4960", "sample": 1, "correct": false, "tokens": 8192}, {"item": "gsm8k_train_4335", "sample": 0, "correct": true, "tokens": 1409}, {"item": "gsm8k_train_4335", "sample": 1, "correct": false, "tokens": 8192}, {"item": "gsm8k_train_3553", "sample": 0, "correct": false, "tokens": 8192}, {"item": "gsm8k_train_3553", "sample": 1, "correct": false, "tokens": 8192}, {"item": "gsm8k_train_5789", "sample": 0, "correct": false, "tokens": 8192}, {"item": "gsm8k_train_5789", "sample": 1, "correct": false, "tokens": 8192}, {"item": "gsm8k_train_1381", "sample": 0, "correct": false, "tokens": 8192}, {"item": "gsm8k_train_1381", "sample": 1, "correct": false, "tokens": 8192}, {"item": "gsm8k_train_1927", "sample": 0, "correct": false, "tokens": 8192}, {"item": "gsm8k_train_1927", "sample": 1, "correct": false, "tokens": 8192}, {"item": "gsm8k_train_5240", "sample": 0, "correct": false

All: {'n': 128, 'original_correct': 13, 'edited_correct': 11, 'gain_pp': np.float64(-1.562), 'paired_se_pp': np.float64(2.713), 'rescued': 5, 'regressed': 7, 'original_cut': 116, 'edited_cut': 117, 'original_mean_tokens': 30207, 'edited_mean_tokens': 30363}gsm8k {'n': 64, 'original_correct': 8, 'edited_correct': 7, 'gain_pp': np.float64(-1.562), 'paired_se_pp': np.float64(4.162), 'rescued': 3, 'regressed': 4, 'original_cut': 56, 'edited_cut': 57, 'original_mean_tokens': 29338, 'edited_mean_tokens': 29717}math {'n': 64, 'original_correct': 5, 'edited_correct': 4, 'gain_pp': np.float64(-1.562), 'paired_se_pp': np.float64(3.516), 'rescued': 2, 'regressed': 3, 'original_cut': 60, 'edited_cut': 60, 'original_mean_tokens': 31075, 'edited_mean_tokens': 31008}original tail repetition mean/median: [0.8585 0.9728]edit_strength_1 tail repetition mean/median: [0.8708 0.9762]gsm8k_train_4335 correct True -> True tokens 32768 -> 1358 edited tail: ch box contains 20 matches.*   Total matches = (Number of boxes) $\times$ (Matches per box)*   Total matches = $60 \times 20$.*   $60 \times 20 = 1200$.
Thus, James has 1200 matches.
\boxed{1200}gsm8k_train_7076 correct True -> False tokens 13476 -> 32768 edited tail: .    Wait, why did I think $Cost_C - Cost_O = 95$?    Wait, $135 - 40 = 95$.    Wait, why did I think $Cost_C - Cost_O = 95$?    Wait, $135 - 40 = 95$.    Wait, why did I think $Cost_C - Cost_O =gsm8k_train_462 correct False -> True tokens 32768 -> 7302 edited tail: of skips Valerie makes:$$ \text{Total Skips} = \text{Skips}_{\text{Roberto}} + \text{Skips}_{\text{Valerie}} $$$$ \text{Total Skips} = 1,050 + 1,200 $$$$ \text{Total Skips} = 2,250 $$
\boxed{2250}gsm8k_train_5989 correct False -> True tokens 32768 -> 9075 edited tail: hases equals \$7.*   $\text{Starting Money} - \text{Total Cost} = \$7$*   $\text{Starting Money} - \$46 = \$7$*   $\text{Starting Money} = \$46 + \$7$*   $\text{Starting Money} = \$53$
\boxed{53}gsm8k_train_1028 correct True -> False tokens 9531 -> 32768 edited tail: hat I did.            *   Wait, is it possible that "20 bars" refers to the number of bars in the container?            *   Yes, that's what I did.            *   Wait, is it possible that "20 barsgsm8k_train_714 correct True -> False tokens 5123 -> 32768 edited tail: per visit" means he gets $0.01 *per visit*?    *   If he gets $0.01 per visit, he gets $300.    *   Wait, what if "gets $0.01 per visit" means he gets $0.01 *per visit*?    *   If he gets $0.01 pergsm8k_train_350 correct True -> False tokens 837 -> 32768 edited tail: re rolls of wrapping paper does Chandler need to sell [for the total available]?"    *   No.    *   Let's check if the question is "How many more rolls of wrapping paper does Chandler need to sell [gsm8k_train_5000 correct False -> True tokens 32768 -> 2169 edited tail: times \$20.00 = \$2.00$$
**Step 3: Calculate the final total bill**
Subtract the discount from the subtotal:$$\$20.00 - \$2.00 = \$18.00$$
The total grocery bill comes to $\$18.00$.
\boxed{18.00}Activation index: {"layers": [18, 19, 23], "mlp_layers": [18, 19, 23], "samples": [{"item": "gsm8k_train_7354", "sample": 0, "correct": false, "tokens": 8192}, {"item": "gsm8k_train_7354", "sample": 1, "correct": false, "tokens": 8192}, {"item": "gsm8k_train_4960", "sample": 0, "correct": false, "tokens": 8192}, {"item": "gsm8k_train_4960", "sample": 1, "correct": false, "tokens": 8192}, {"item": "gsm8k_train_4335", "sample": 0, "correct": true, "tokens": 1409}, {"item": "gsm8k_train_4335", "sample": 1, "correct": false, "tokens": 8192}, {"item": "gsm8k_train_3553", "sample": 0, "correct": false, "tokens": 8192}, {"item": "gsm8k_train_3553", "sample": 1, "correct": false, "tokens": 8192}, {"item": "gsm8k_train_5789", "sample": 0, "correct": false, "tokens": 8192}, {"item": "gsm8k_train_5789", "sample": 1, "correct": false, "tokens": 8192}, {"item": "gsm8k_train_1381", "sample": 0, "correct": false, "tokens": 8192}, {"item": "gsm8k_train_1381", "sample": 1, "correct": false, "tokens": 8192}, {"item": "gsm8k_train_1927", "sample": 0, "correct": false, "tokens": 8192}, {"item": "gsm8k_train_1927", "sample": 1, "correct": false, "tokens": 8192}, {"item": "gsm8k_train_5240", "sample": 0, "correct": false

python

# Hypothesis: the four-channel correlation was not causal; a broader weight/activation audit may reveal a more specific repetition feedback circuit.for name in list(edited()):    drop_edit(name)print("Edits cleared:", edited())print("Layer 18 tensor names:", [n for n in NAMES if ".layers.18." in n])print("Layer 19 tensor names:", [n for n in NAMES if ".layers.19." in n])print("Teacher-forced files:",os.listdir("/work/fwd_3"))print("TF metadata:",open("/work/fwd_3/meta.json").read()[:2400])print("Generation activation files:",os.listdir("/work/gen_1_acts")[:35])print("Score object:",type(scores).__name__,repr(scores)[:1300])print("Selected global shapes:",{k:(tuple(v.shape) if hasattr(v,"shape") else type(v).__name__) for k,v in globals().copy().items() if k in ["M1","E","W","direction","delta","down_cache","mean_good","mean_bad","FROZEN"]})for l in range(24):    n=f"model.language_model.layers.{l}.mlp.down_proj.weight"    ww=load_original(n)    cn=ww.norm(dim=0)    print(l,"down norm",round(float(ww.norm()),2),"column norms median/max",round(float(cn.median()),3),round(float(cn.max()),3),          "top columns",torch.topk(cn,4).indices.tolist())with open("/work/notes.md","a") as f:    f.write("Four-channel ablation rejected: no repetition reduction or accuracy gain. All edits cleared. Seven forward passes remain.\n")

# Hypothesis: the four-channel correlation was not causal; a broader weight/activation audit may reveal a more specific repetition feedback circuit.for name in list(edited()):    drop_edit(name)print("Edits cleared:", edited())print("Layer 18 tensor names:", [n for n in NAMES if ".layers.18." in n])print("Layer 19 tensor names:", [n for n in NAMES if ".layers.19." in n])print("Teacher-forced files:",os.listdir("/work/fwd_3"))print("TF metadata:",open("/work/fwd_3/meta.json").read()[:2400])print("Generation activation files:",os.listdir("/work/gen_1_acts")[:35])print("Score object:",type(scores).__name__,repr(scores)[:1300])print("Selected global shapes:",{k:(tuple(v.shape) if hasattr(v,"shape") else type(v).__name__) for k,v in globals().copy().items() if k in ["M1","E","W","direction","delta","down_cache","mean_good","mean_bad","FROZEN"]})for l in range(24):    n=f"model.language_model.layers.{l}.mlp.down_proj.weight"    ww=load_original(n)    cn=ww.norm(dim=0)    print(l,"down norm",round(float(ww.norm()),2),"column norms median/max",round(float(cn.median()),3),round(float(cn.max()),3),          "top columns",torch.topk(cn,4).indices.tolist())with open("/work/notes.md","a") as f:    f.write("Four-channel ablation rejected: no repetition reduction or accuracy gain. All edits cleared. Seven forward passes remain.\n")

OUTPUT of Python run 41 of at most 300:

Edits cleared: []Layer 18 tensor names: ['model.language_model.layers.18.input_layernorm.weight', 'model.language_model.layers.18.linear_attn.A_log', 'model.language_model.layers.18.linear_attn.conv1d.weight', 'model.language_model.layers.18.linear_attn.dt_bias', 'model.language_model.layers.18.linear_attn.in_proj_a.weight', 'model.language_model.layers.18.linear_attn.in_proj_b.weight', 'model.language_model.layers.18.linear_attn.in_proj_qkv.weight', 'model.language_model.layers.18.linear_attn.in_proj_z.weight', 'model.language_model.layers.18.linear_attn.norm.weight', 'model.language_model.layers.18.linear_attn.out_proj.weight', 'model.language_model.layers.18.mlp.down_proj.weight', 'model.language_model.layers.18.mlp.gate_proj.weight', 'model.language_model.layers.18.mlp.up_proj.weight', 'model.language_model.layers.18.post_attention_layernorm.weight']Layer 19 tensor names: ['model.language_model.layers.19.input_layernorm.weight', 'model.language_model.layers.19.mlp.down_proj.weight', 'model.language_model.layers.19.mlp.gate_proj.weight', 'model.language_model.layers.19.mlp.up_proj.weight', 'model.language_model.layers.19.post_attention_layernorm.weight', 'model.language_model.layers.19.self_attn.k_norm.weight', 'model.language_model.layers.19.self_attn.k_proj.weight', 'model.language_model.layers.19.self_attn.o_proj.weight', 'model.language_model.layers.19.self_attn.q_norm.weight', 'model.language_model.layers.19.self_attn.q_proj.weight', 'model.language_model.layers.19.self_attn.v_proj.weight']Teacher-forced files: ['token_logprobs.npy', 'mlp_act_7.npy', 'mlp_act_0.npy', 'mlp_act_9.npy', 'hidden_20.npy', 'hidden_12.npy', 'mlp_act_15.npy', 'mlp_act_12.npy', 'hidden_0.npy', 'topk_logprobs.npy', 'mlp_act_20.npy', 'topk_ids.npy', 'mlp_act_8.npy', 'mlp_act_1.npy', 'mlp_act_6.npy', 'hidden_8.npy', 'mlp_act_21.npy', 'mlp_act_13.npy', 'mlp_act_14.npy', 'mlp_act_22.npy', 'mlp_act_19.npy', 'mlp_act_17.npy', 'mlp_act_10.npy', 'mlp_act_5.npy', 'mlp_act_2.npy', 'input_ids.npy', 'mlp_act_11.npy', 'mlp_act_16.npy', 'mlp_act_18.npy', 'hidden_4.npy', 'mlp_act_23.npy', 'mlp_act_3.npy', 'mlp_act_4.npy', 'hidden_24.npy', 'hidden_16.npy', 'meta.json']TF metadata: { "texts": [  "<|im_start|>user\nJames has 5 dozen boxes of matches. Each box contains 20 matches. How many matches does James have in total?\nPlease reason step by step, and put your final answer within \\boxed{}.<|im_end|>\n<|im_start|>assistant\n<think>\n\n</think>\n\n<think>\nHere's my thought process for solving this problem:\n\n1. **Analyze the Request:**\n   * Given: James has 5 dozen boxes of matches. Each box contains 20 matches.\n   * Goal: Calculate the total number of matches.\n\n2. **Convert dozens to boxes:**\n   * One dozen is 12.\n   * Five dozen is 5 times 12 = 60 boxes.\n\n3. **Calculate the total:**\n   * Each box contains 20 matches.\n   * Total matches = 60 times 20 = 1200.\n\n4. **Check the calculation:**\n   * Alternatively, one dozen boxes contains 12 times 20 = 240 matches.\n   * Five dozen boxes contains 5 times 240 = 1200 matches.\n   * Both calculations agree.\n\n   * Wait, 5 dozen means 60 boxes. Each box contains 20 matches. Therefore, 60 times 20 is 1200 matches.\n   * Wait, 5 dozen means 60 boxes. Each box contains 20 matches. Therefore, 60 times 20 is 1200 matches.\n   * Wait, 5 dozen means 60 boxes. Each box contains 20 matches. Therefore, 60 times 20 is 1200 matches.\n   * Wait, 5 dozen means 60 boxes. Each box contains 20 matches. Therefore, 60 times 20 is 1200 matches.\n   * Wait, 5 dozen means 60 boxes. Each box contains 20 matches. Therefore, 60 times 20 is 1200 matches.\n   * Wait, 5 dozen means 60 boxes. Each box contains 20 matches. Therefore, 60 times 20 is 1200 matches.\n   * Wait, 5 dozen means 60 boxes. Each box contains 20 matches. Therefore, 60 times 20 is 1200 matches.\n   * Wait, 5 dozen means 60 boxes. Each box contains 20 matches. Therefore, 60 times 20 is 1200 matches.\n\n   I will write the response.\n   I will write the response.\n   I will write the response.\n   I will write the response.\n   I will write the response.\n   I will write the response.\n   I will write the response.\n   I will write the response.\n\nA different check is to group the 60 boxes into 6 groups of 10. Each group has 200 matches, so 6 groups have 1200 matches. The units are matches rather than boxes. No boxes have been removed.<|im_end|>\n",  "<|im_start|>user\nCompute $\\tan(225^\\circ)$.\nPlease reason step by step, and put your final answer within \\boxed{}.<|im_end|>\n<|im_start|>assistant\n<think>\n\n</think>\n\n<think>\Generation activation files: ['mlp_mean_answer.npy', 'hidden_mean_answer.npy', 'hidden_last_prompt.npy', 'mlp_last_prompt.npy', 'index.json']Score object: ndarray array([ 0.0135,  0.0163,  0.0145,  0.0149,  0.0151,  0.0141,  0.017 ,  0.0159,  0.0128,  0.0159,  0.0216,  0.0199,  0.0208,  0.017 ,  0.0236,  0.0178,  0.0199,        0.0157,  0.0095,  0.0171,  0.0117,  0.0199,  0.014 ,  0.016 ,  0.0107,  0.0134,  0.0141,  0.0092,  0.0067,  0.0041,  0.0134,  0.0034,  0.0117, -0.0101,       -0.0069], dtype=float32)Selected global shapes: {'FROZEN': 'set', 'M1': (256, 23, 3584), 'mean_bad': (23, 3584), 'mean_good': (23, 3584), 'E': (248320, 1024), 'down_cache': 'dict', 'W': (1024, 3584), 'direction': (1024,), 'delta': (128,)}0 down norm 16.28 column norms median/max 0.274 0.302 top columns [204, 2557, 282, 1833]1 down norm 16.45 column norms median/max 0.276 0.324 top columns [1992, 2332, 14, 852]2 down norm 15.77 column norms median/max 0.266 0.339 top columns [37, 338, 1592, 556]3 down norm 15.47 column norms median/max 0.259 0.323 top columns [3059, 1102, 76, 740]4 down norm 15.58 column norms median/max 0.262 0.379 top columns [172, 810, 678, 212]5 down norm 15.81 column norms median/max 0.265 0.355 top columns [90, 126, 135, 107]6 down norm 15.69 column norms median/max 0.26 0.454 top columns [172, 182, 496, 198]7 down norm 15.77 column norms median/max 0.26 0.375 top columns [60, 223, 27, 140]8 down norm 15.4 column norms median/max 0.256 0.362 top columns [94, 16, 36, 35]9 down norm 15.55 column norms median/max 0.259 0.409 top columns [2201, 458, 104, 76]10 down norm 15.24 column norms median/max 0.252 0.475 top columns [1725, 510, 684, 773]11 down norm 15.5 column norms median/max 0.256 0.45 top columns [111, 159, 143, 2705]12 down norm 15.22 column norms median/max 0.255 0.387 top columns [628, 2824, 597, 635]13 down norm 15.35 column norms median/max 0.256 0.455 top columns [540, 516, 336, 861]14 down norm 15.14 column norms median/max 0.254 0.448 top columns [1438, 1534, 1510, 2237]15 down norm 16.44 column norms median/max 0.273 0.494 top columns [2974, 2068, 1590, 1578]16 down norm 16.37 column norms median/max 0.277 0.47 top columns [2949, 2142, 3405, 7]17 down norm 16.39 column norms median/max 0.277 0.373 top columns [2886, 2563, 3455, 2155]18 down norm 16.43 column norms median/max 0.278 0.362 top columns [3502, 351, 2028, 3301]19 down norm 17.26 column norms median/max 0.293 0.427 top columns [2681, 1776, 2163, 1736]20 down norm 17.51 column norms median/max 0.297 0.405 top columns [1458, 2099, 1642, 2705]21 down norm 17.7 column norms median/max 0.3 0.469 top columns [3519, 2126, 3384, 2842]22 down norm 17.52 column norms median/max 0.297 0.453 top columns [1460, 1700, 1455, 2294]23 down norm 16.42 column norms median/max 0.27 0.551 top columns [1404, 2471, 3051, 1303]

Edits cleared: []Layer 18 tensor names: ['model.language_model.layers.18.input_layernorm.weight', 'model.language_model.layers.18.linear_attn.A_log', 'model.language_model.layers.18.linear_attn.conv1d.weight', 'model.language_model.layers.18.linear_attn.dt_bias', 'model.language_model.layers.18.linear_attn.in_proj_a.weight', 'model.language_model.layers.18.linear_attn.in_proj_b.weight', 'model.language_model.layers.18.linear_attn.in_proj_qkv.weight', 'model.language_model.layers.18.linear_attn.in_proj_z.weight', 'model.language_model.layers.18.linear_attn.norm.weight', 'model.language_model.layers.18.linear_attn.out_proj.weight', 'model.language_model.layers.18.mlp.down_proj.weight', 'model.language_model.layers.18.mlp.gate_proj.weight', 'model.language_model.layers.18.mlp.up_proj.weight', 'model.language_model.layers.18.post_attention_layernorm.weight']Layer 19 tensor names: ['model.language_model.layers.19.input_layernorm.weight', 'model.language_model.layers.19.mlp.down_proj.weight', 'model.language_model.layers.19.mlp.gate_proj.weight', 'model.language_model.layers.19.mlp.up_proj.weight', 'model.language_model.layers.19.post_attention_layernorm.weight', 'model.language_model.layers.19.self_attn.k_norm.weight', 'model.language_model.layers.19.self_attn.k_proj.weight', 'model.language_model.layers.19.self_attn.o_proj.weight', 'model.language_model.layers.19.self_attn.q_norm.weight', 'model.language_model.layers.19.self_attn.q_proj.weight', 'model.language_model.layers.19.self_attn.v_proj.weight']Teacher-forced files: ['token_logprobs.npy', 'mlp_act_7.npy', 'mlp_act_0.npy', 'mlp_act_9.npy', 'hidden_20.npy', 'hidden_12.npy', 'mlp_act_15.npy', 'mlp_act_12.npy', 'hidden_0.npy', 'topk_logprobs.npy', 'mlp_act_20.npy', 'topk_ids.npy', 'mlp_act_8.npy', 'mlp_act_1.npy', 'mlp_act_6.npy', 'hidden_8.npy', 'mlp_act_21.npy', 'mlp_act_13.npy', 'mlp_act_14.npy', 'mlp_act_22.npy', 'mlp_act_19.npy', 'mlp_act_17.npy', 'mlp_act_10.npy', 'mlp_act_5.npy', 'mlp_act_2.npy', 'input_ids.npy', 'mlp_act_11.npy', 'mlp_act_16.npy', 'mlp_act_18.npy', 'hidden_4.npy', 'mlp_act_23.npy', 'mlp_act_3.npy', 'mlp_act_4.npy', 'hidden_24.npy', 'hidden_16.npy', 'meta.json']TF metadata: { "texts": [  "<|im_start|>user\nJames has 5 dozen boxes of matches. Each box contains 20 matches. How many matches does James have in total?\nPlease reason step by step, and put your final answer within \\boxed{}.<|im_end|>\n<|im_start|>assistant\n<think>\n\n</think>\n\n<think>\nHere's my thought process for solving this problem:\n\n1. **Analyze the Request:**\n   * Given: James has 5 dozen boxes of matches. Each box contains 20 matches.\n   * Goal: Calculate the total number of matches.\n\n2. **Convert dozens to boxes:**\n   * One dozen is 12.\n   * Five dozen is 5 times 12 = 60 boxes.\n\n3. **Calculate the total:**\n   * Each box contains 20 matches.\n   * Total matches = 60 times 20 = 1200.\n\n4. **Check the calculation:**\n   * Alternatively, one dozen boxes contains 12 times 20 = 240 matches.\n   * Five dozen boxes contains 5 times 240 = 1200 matches.\n   * Both calculations agree.\n\n   * Wait, 5 dozen means 60 boxes. Each box contains 20 matches. Therefore, 60 times 20 is 1200 matches.\n   * Wait, 5 dozen means 60 boxes. Each box contains 20 matches. Therefore, 60 times 20 is 1200 matches.\n   * Wait, 5 dozen means 60 boxes. Each box contains 20 matches. Therefore, 60 times 20 is 1200 matches.\n   * Wait, 5 dozen means 60 boxes. Each box contains 20 matches. Therefore, 60 times 20 is 1200 matches.\n   * Wait, 5 dozen means 60 boxes. Each box contains 20 matches. Therefore, 60 times 20 is 1200 matches.\n   * Wait, 5 dozen means 60 boxes. Each box contains 20 matches. Therefore, 60 times 20 is 1200 matches.\n   * Wait, 5 dozen means 60 boxes. Each box contains 20 matches. Therefore, 60 times 20 is 1200 matches.\n   * Wait, 5 dozen means 60 boxes. Each box contains 20 matches. Therefore, 60 times 20 is 1200 matches.\n\n   I will write the response.\n   I will write the response.\n   I will write the response.\n   I will write the response.\n   I will write the response.\n   I will write the response.\n   I will write the response.\n   I will write the response.\n\nA different check is to group the 60 boxes into 6 groups of 10. Each group has 200 matches, so 6 groups have 1200 matches. The units are matches rather than boxes. No boxes have been removed.<|im_end|>\n",  "<|im_start|>user\nCompute $\\tan(225^\\circ)$.\nPlease reason step by step, and put your final answer within \\boxed{}.<|im_end|>\n<|im_start|>assistant\n<think>\n\n</think>\n\n<think>\Generation activation files: ['mlp_mean_answer.npy', 'hidden_mean_answer.npy', 'hidden_last_prompt.npy', 'mlp_last_prompt.npy', 'index.json']Score object: ndarray array([ 0.0135,  0.0163,  0.0145,  0.0149,  0.0151,  0.0141,  0.017 ,  0.0159,  0.0128,  0.0159,  0.0216,  0.0199,  0.0208,  0.017 ,  0.0236,  0.0178,  0.0199,        0.0157,  0.0095,  0.0171,  0.0117,  0.0199,  0.014 ,  0.016 ,  0.0107,  0.0134,  0.0141,  0.0092,  0.0067,  0.0041,  0.0134,  0.0034,  0.0117, -0.0101,       -0.0069], dtype=float32)Selected global shapes: {'FROZEN': 'set', 'M1': (256, 23, 3584), 'mean_bad': (23, 3584), 'mean_good': (23, 3584), 'E': (248320, 1024), 'down_cache': 'dict', 'W': (1024, 3584), 'direction': (1024,), 'delta': (128,)}0 down norm 16.28 column norms median/max 0.274 0.302 top columns [204, 2557, 282, 1833]1 down norm 16.45 column norms median/max 0.276 0.324 top columns [1992, 2332, 14, 852]2 down norm 15.77 column norms median/max 0.266 0.339 top columns [37, 338, 1592, 556]3 down norm 15.47 column norms median/max 0.259 0.323 top columns [3059, 1102, 76, 740]4 down norm 15.58 column norms median/max 0.262 0.379 top columns [172, 810, 678, 212]5 down norm 15.81 column norms median/max 0.265 0.355 top columns [90, 126, 135, 107]6 down norm 15.69 column norms median/max 0.26 0.454 top columns [172, 182, 496, 198]7 down norm 15.77 column norms median/max 0.26 0.375 top columns [60, 223, 27, 140]8 down norm 15.4 column norms median/max 0.256 0.362 top columns [94, 16, 36, 35]9 down norm 15.55 column norms median/max 0.259 0.409 top columns [2201, 458, 104, 76]10 down norm 15.24 column norms median/max 0.252 0.475 top columns [1725, 510, 684, 773]11 down norm 15.5 column norms median/max 0.256 0.45 top columns [111, 159, 143, 2705]12 down norm 15.22 column norms median/max 0.255 0.387 top columns [628, 2824, 597, 635]13 down norm 15.35 column norms median/max 0.256 0.455 top columns [540, 516, 336, 861]14 down norm 15.14 column norms median/max 0.254 0.448 top columns [1438, 1534, 1510, 2237]15 down norm 16.44 column norms median/max 0.273 0.494 top columns [2974, 2068, 1590, 1578]16 down norm 16.37 column norms median/max 0.277 0.47 top columns [2949, 2142, 3405, 7]17 down norm 16.39 column norms median/max 0.277 0.373 top columns [2886, 2563, 3455, 2155]18 down norm 16.43 column norms median/max 0.278 0.362 top columns [3502, 351, 2028, 3301]19 down norm 17.26 column norms median/max 0.293 0.427 top columns [2681, 1776, 2163, 1736]20 down norm 17.51 column norms median/max 0.297 0.405 top columns [1458, 2099, 1642, 2705]21 down norm 17.7 column norms median/max 0.3 0.469 top columns [3519, 2126, 3384, 2842]22 down norm 17.52 column norms median/max 0.297 0.453 top columns [1460, 1700, 1455, 2294]23 down norm 16.42 column norms median/max 0.27 0.551 top columns [1404, 2471, 3051, 1303]

python

# Hypothesis: repetition may be amplified by a small set of abnormal attention gates or normalization coordinates, missed by the first MLP ablation.for l in range(24):    prefix=f"model.language_model.layers.{l}."    stats=[]    for suffix in ["input_layernorm.weight","post_attention_layernorm.weight"]:        ww=load_original(prefix+suffix)        stats.append((suffix.split(".")[0],*[round(float(z),3) for z in (ww.min(),ww.median(),ww.max())],                      torch.topk(ww.abs(),3).indices.tolist()))    print(l,stats)    if l%4!=3:        al=load_original(prefix+"linear_attn.A_log")        dt=load_original(prefix+"linear_attn.dt_bias")        zz=load_original(prefix+"linear_attn.norm.weight")        print("  recurrent A_log",np.round(al.numpy(),2).tolist(),"dt",np.round(dt.numpy(),2).tolist(),              "gated_norm min/median/max",np.round([zz.min(),zz.median(),zz.max()],3))    else:        for suf in ["self_attn.q_norm.weight","self_attn.k_norm.weight"]:            ww=load_original(prefix+suf)            print(" ",suf,"min/median/max",np.round([ww.min(),ww.median(),ww.max()],3))print("TF shapes:", {fn:np.load("/work/fwd_3/"+fn,mmap_mode="r").shape for fn in ["input_ids.npy","hidden_24.npy","mlp_act_23.npy","topk_ids.npy"]})print("Index metadata:",{k:v for k,v in json.load(open("/work/gen_1_acts/index.json")).items() if k!="samples"})

# Hypothesis: repetition may be amplified by a small set of abnormal attention gates or normalization coordinates, missed by the first MLP ablation.for l in range(24):    prefix=f"model.language_model.layers.{l}."    stats=[]    for suffix in ["input_layernorm.weight","post_attention_layernorm.weight"]:        ww=load_original(prefix+suffix)        stats.append((suffix.split(".")[0],*[round(float(z),3) for z in (ww.min(),ww.median(),ww.max())],                      torch.topk(ww.abs(),3).indices.tolist()))    print(l,stats)    if l%4!=3:        al=load_original(prefix+"linear_attn.A_log")        dt=load_original(prefix+"linear_attn.dt_bias")        zz=load_original(prefix+"linear_attn.norm.weight")        print("  recurrent A_log",np.round(al.numpy(),2).tolist(),"dt",np.round(dt.numpy(),2).tolist(),              "gated_norm min/median/max",np.round([zz.min(),zz.median(),zz.max()],3))    else:        for suf in ["self_attn.q_norm.weight","self_attn.k_norm.weight"]:            ww=load_original(prefix+suf)            print(" ",suf,"min/median/max",np.round([ww.min(),ww.median(),ww.max()],3))print("TF shapes:", {fn:np.load("/work/fwd_3/"+fn,mmap_mode="r").shape for fn in ["input_ids.npy","hidden_24.npy","mlp_act_23.npy","topk_ids.npy"]})print("Index metadata:",{k:v for k,v in json.load(open("/work/gen_1_acts/index.json")).items() if k!="samples"})

OUTPUT of Python run 42 of at most 300:

0 [('input_layernorm', -0.115, 0.205, 1.0, [0, 119, 38]), ('post_attention_layernorm', -0.357, 0.115, 0.275, [0, 523, 521])]  recurrent A_log [0.25999999046325684, -2.0299999713897705, -2.119999885559082, -0.5699999928474426, -2.609999895095825, -2.25, -1.7999999523162842, -2.9800000190734863, -6.590000152587891, -0.6600000262260437, -1.7799999713897705, -0.9100000262260437, -1.649999976158142, -1.4500000476837158, -1.899999976158142, -2.609999895095825] dt [2.549999952316284, -5.590000152587891, -4.619999885559082, 8.380000114440918, 3.640000104904175, -5.03000020980835, -5.059999942779541, -1.0800000429153442, -13.0600004196167, -5.440000057220459, -4.809999942779541, 6.440000057220459, -4.940000057220459, 7.590000152587891, -6.059999942779541, -3.2300000190734863] gated_norm min/median/max [0.523 0.973 1.062]1 [('input_layernorm', -0.062, 0.439, 0.789, [46, 66, 74]), ('post_attention_layernorm', -0.273, 0.192, 0.479, [536, 416, 816])]  recurrent A_log [-1.7200000286102295, 0.07999999821186066, 0.14000000059604645, -1.2000000476837158, 0.5699999928474426, -1.159999966621399, 0.4300000071525574, -2.0899999141693115, -1.1799999475479126, -0.5199999809265137, -2.25, -0.23000000417232513, -1.4600000381469727, 0.1899999976158142, -2.1600000858306885, 2.359999895095825] dt [-1.7599999904632568, 0.8500000238418579, 2.0299999713897705, 1.7999999523162842, 0.07999999821186066, 0.9399999976158142, 0.6600000262260437, -6.0, 1.9900000095367432, 1.6699999570846558, -4.46999979019165, 2.5199999809265137, -4.380000114440918, 1.8799999952316284, -4.46999979019165, -5.659999847412109] gated_norm min/median/max [0.449 0.953 1.078]2 [('input_layernorm', -0.043, 0.447, 0.918, [66, 74, 22]), ('post_attention_layernorm', -0.218, 0.279, 0.629, [816, 693, 900])]  recurrent A_log [-0.10999999940395355, -1.2699999809265137, -1.2100000381469727, -0.4300000071525574, -1.5800000429153442, -1.149999976158142, -0.8100000023841858, -1.2400000095367432, 0.44999998807907104, -1.0700000524520874, -0.8399999737739563, -1.8799999952316284, -2.880000114440918, -0.019999999552965164, -1.2000000476837158, -2.6700000762939453] dt [1.6699999570846558, -2.190000057220459, -0.09000000357627869, 0.7200000286102295, -2.5299999713897705, -5.46999979019165, 1.7599999904632568, -0.4399999976158142, -0.3700000047683716, -0.8700000047683716, 1.409999966621399, -4.619999885559082, -2.7799999713897705, 1.1399999856948853, -5.03000020980835, -3.6600000858306885] gated_norm min/median/max [0.355 0.961 1.086]3 [('input_layernorm', -0.035, 0.355, 0.84, [46, 78, 30]), ('post_attention_layernorm', -0.204, 0.262, 0.463, [900, 838, 693])]  self_attn.q_norm.weight min/median/max [-0.211  0.391  1.227]  self_attn.k_norm.weight min/median/max [-0.898  0.385  1.633]4 [('input_layernorm', -0.01, 0.512, 0.84, [66, 74, 88]), ('post_attention_layernorm', -0.262, 0.252, 0.373, [818, 693, 900])]  recurrent A_log [-0.8799999952316284, -0.029999999329447746, -1.159999966621399, -0.6399999856948853, -0.8999999761581421, -2.5899999141693115, -1.350000023841858, -0.1599999964237213, 0.6000000238418579, -1.309999942779541, -0.9100000262260437, -0.6200000047683716, -2.9800000190734863, 0.5799999833106995, -1.3899999856948853, -2.7799999713897705] dt [0.03999999910593033, 2.25, -4.46999979019165, 1.8899999856948853, -5.380000114440918, -5.53000020980835, -5.28000020980835, 0.9300000071525574, 0.10999999940395355, -3.049999952316284, -0.25999999046325684, -5.5, -3.3399999141693115, -5.25, -0.11999999731779099, -4.03000020980835] gated_norm min/median/max [0.348 0.91  1.078]5 [('input_layernorm', -0.188, 0.473, 0.773, [66, 88, 42]), ('post_attention_layernorm', -0.355, 0.239, 0.365, [818, 481, 531])]  recurrent A_log [-0.27000001072883606, -2.109999895095825, -1.3700000047683716, -0.8700000047683716, -2.559999942779541, 0.6600000262260437, -0.07999999821186066, 2.0, -2.1600000858306885, -1.6699999570846558, -3.1700000762939453, -0.14000000059604645, -2.190000057220459, 0.23999999463558197, -1.590000033378601, -2.0799999237060547] dt [-0.12999999523162842, -3.859999895095825, -1.149999976158142, -5.190000057220459, -4.03000020980835, -6.5, -0.07000000029802322, -4.159999847412109, -3.559999942779541, -3.8399999141693115, -2.9700000286102295, -1.6200000047683716, -3.950000047683716, -0.800000011920929, 1.4199999570846558, -3.4800000190734863] gated_norm min/median/max [0.473 0.961 1.117]6 [('input_layernorm', -0.305, 0.428, 0.684, [88, 66, 74]), ('post_attention_layernorm', -0.391, 0.188, 0.293, [523, 443, 545])]  recurrent A_log [-1.7000000476837158, -0.30000001192092896, -1.190000057220459, -0.5899999737739563, -0.10000000149011612, -1.2999999523162842, -0.8700000047683716, -2.25, -1.1100000143051147, 0.44999998807907104, -0.3799999952316284, -0.6299999952316284, -0.4399999976158142, -0.47999998927116394, -0.5299999713897705, -2.450000047683716] dt [-1.0499999523162842, -1.340000033378601, -0.07000000029802322, -1.3300000429153442, -1.4800000190734863, -3.75, -5.96999979019165, -3.700000047683716, -3.119999885559082, -0.8399999737739563, -1.7200000286102295, 2.0299999713897705, -6.0, -1.7899999618530273, -0.05000000074505806, 2.0199999809265137] gated_norm min/median/max [0.531 1.    1.141]7 [('input_layernorm', -0.377, 0.146, 0.523, [46, 123, 545]), ('post_attention_layernorm', -0.447, 0.191, 0.309, [523, 481, 443])]  self_attn.q_norm.weight min/median/max [-0.239  0.5    1.023]  self_attn.k_norm.weight min/median/max [-0.165  0.492  1.062]8 [('input_layernorm', -0.422, 0.453, 0.648, [74, 66, 909]), ('post_attention_layernorm', -0.496, 0.175, 0.309, [523, 521, 443])]  recurrent A_log [-1.0499999523162842, -1.2599999904632568, -1.2100000381469727, -1.8899999856948853, -1.7999999523162842, -0.5, -1.4900000095367432, -0.9800000190734863, -0.14000000059604645, -2.299999952316284, -1.399999976158142, -1.0199999809265137, -0.9300000071525574, -2.7200000286102295, -0.41999998688697815, -1.0199999809265137] dt [-0.8799999952316284, -1.9800000190734863, -4.46999979019165, -3.7300000190734863, -3.0199999809265137, 0.20999999344348907, -4.21999979019165, -0.9100000262260437, -2.5, -4.5, -0.15000000596046448, 0.25, -4.690000057220459, -4.059999942779541, -1.1200000047683716, -0.20000000298023224] gated_norm min/median/max [0.617 1.016 1.156]9 [('input_layernorm', -0.445, 0.436, 0.664, [812, 951, 739]), ('post_attention_layernorm', -0.516, 0.154, 0.299, [523, 521, 443])]  recurrent A_log [-0.8999999761581421, -1.9900000095367432, -1.0800000429153442, -1.690000057220459, -1.2999999523162842, -2.5199999809265137, -0.7599999904632568, -1.909999966621399, -1.4500000476837158, 0.0, -2.450000047683716, -1.2100000381469727, -2.1700000762939453, -1.7100000381469727, -2.0199999809265137, -2.450000047683716] dt [-3.299999952316284, -3.9100000858306885, -1.899999976158142, -1.1699999570846558, -1.0499999523162842, -4.71999979019165, -1.100000023841858, -3.950000047683716, -2.390000104904175, -0.8100000023841858, 1.409999966621399, 1.6100000143051147, -4.21999979019165, -3.690000057220459, -4.21999979019165, -3.25] gated_norm min/median/max [0.711 1.031 1.195]10 [('input_layernorm', -0.494, 0.369, 0.645, [909, 816, 835]), ('post_attention_layernorm', -0.516, 0.123, 0.248, [523, 807, 443])]  recurrent A_log [-0.27000001072883606, 1.7599999904632568, -1.1699999570846558, -1.590000033378601, -0.5099999904632568, -1.0700000524520874, -1.1799999475479126, -1.0099999904632568, -1.090000033378601, -1.7100000381469727, -1.590000033378601, -1.590000033378601, -1.4500000476837158, -1.100000023841858, -1.4299999475479126, -1.8799999952316284] dt [-1.149999976158142, -7.0, 3.190000057220459, -1.5499999523162842, -1.75, -1.5199999809265137, -1.600000023841858, -0.3799999952316284, -1.8799999952316284, -2.5799999237060547, -3.75, -3.7699999809265137, -4.909999847412109, -0.9399999976158142, -1.7699999809265137, -1.2999999523162842] gated_norm min/median/max [0.824 1.047 1.156]11 [('input_layernorm', -0.605, 0.209, 0.699, [46, 523, 807]), ('post_attention_layernorm', -0.436, 0.151, 0.309, [523, 531, 807])]  self_attn.q_norm.weight min/median/max [-1.016  0.645  1.133]  self_attn.k_norm.weight min/median/max [-0.996  0.641  1.312]12 [('input_layernorm', -0.25, 0.551, 0.848, [74, 835, 816]), ('post_attention_layernorm', -0.322, 0.169, 0.338, [1004, 523, 816])]  recurrent A_log [-1.600000023841858, -0.5299999713897705, -2.2699999809265137, -1.2599999904632568, -1.3200000524520874, 0.0, -2.0299999713897705, -3.0299999713897705, -2.200000047683716, -0.7699999809265137, -1.7999999523162842, -0.05000000074505806, -1.7799999713897705, -2.0799999237060547, -2.049999952316284, -1.1100000143051147] dt [-5.0, -1.1699999570846558, -3.8399999141693115, -4.75, -1.9800000190734863, -6.659999847412109, -3.859999895095825, -3.4700000286102295, -4.590000152587891, -1.9199999570846558, -1.340000033378601, -0.5699999928474426, -4.96999979019165, -5.059999942779541, -1.2899999618530273, -1.2000000476837158] gated_norm min/median/max [0.746 1.008 1.141]13 [('input_layernorm', -0.264, 0.48, 0.836, [826, 66, 818]), ('post_attention_layernorm', -0.231, 0.179, 0.369, [1004, 1000, 927])]  recurrent A_log [-1.309999942779541, 0.14000000059604645, -0.6899999976158142, -1.4299999475479126, -1.090000033378601, -1.7000000476837158, -0.4399999976158142, -2.200000047683716, -0.9200000166893005, -1.5399999618530273, -1.0, -1.8300000429153442, -1.340000033378601, -0.8999999761581421, -1.409999966621399, -1.9900000095367432] dt [-1.4500000476837158, -0.5400000214576721, 0.3100000023841858, -1.659999966621399, -5.340000152587891, -2.6700000762939453, -1.149999976158142, 2.190000057220459, -0.6299999952316284, -0.009999999776482582, -1.7300000190734863, -2.2699999809265137, -0.8799999952316284, -3.0899999141693115, -3.119999885559082, -1.8799999952316284] gated_norm min/median/max [0.789 1.039 1.148]14 [('input_layernorm', -0.198, 0.424, 0.699, [74, 904, 909]), ('post_attention_layernorm', -0.096, 0.181, 0.34, [1004, 81, 162])]  rec[output cut at 10,000 characters]

0 [('input_layernorm', -0.115, 0.205, 1.0, [0, 119, 38]), ('post_attention_layernorm', -0.357, 0.115, 0.275, [0, 523, 521])]  recurrent A_log [0.25999999046325684, -2.0299999713897705, -2.119999885559082, -0.5699999928474426, -2.609999895095825, -2.25, -1.7999999523162842, -2.9800000190734863, -6.590000152587891, -0.6600000262260437, -1.7799999713897705, -0.9100000262260437, -1.649999976158142, -1.4500000476837158, -1.899999976158142, -2.609999895095825] dt [2.549999952316284, -5.590000152587891, -4.619999885559082, 8.380000114440918, 3.640000104904175, -5.03000020980835, -5.059999942779541, -1.0800000429153442, -13.0600004196167, -5.440000057220459, -4.809999942779541, 6.440000057220459, -4.940000057220459, 7.590000152587891, -6.059999942779541, -3.2300000190734863] gated_norm min/median/max [0.523 0.973 1.062]1 [('input_layernorm', -0.062, 0.439, 0.789, [46, 66, 74]), ('post_attention_layernorm', -0.273, 0.192, 0.479, [536, 416, 816])]  recurrent A_log [-1.7200000286102295, 0.07999999821186066, 0.14000000059604645, -1.2000000476837158, 0.5699999928474426, -1.159999966621399, 0.4300000071525574, -2.0899999141693115, -1.1799999475479126, -0.5199999809265137, -2.25, -0.23000000417232513, -1.4600000381469727, 0.1899999976158142, -2.1600000858306885, 2.359999895095825] dt [-1.7599999904632568, 0.8500000238418579, 2.0299999713897705, 1.7999999523162842, 0.07999999821186066, 0.9399999976158142, 0.6600000262260437, -6.0, 1.9900000095367432, 1.6699999570846558, -4.46999979019165, 2.5199999809265137, -4.380000114440918, 1.8799999952316284, -4.46999979019165, -5.659999847412109] gated_norm min/median/max [0.449 0.953 1.078]2 [('input_layernorm', -0.043, 0.447, 0.918, [66, 74, 22]), ('post_attention_layernorm', -0.218, 0.279, 0.629, [816, 693, 900])]  recurrent A_log [-0.10999999940395355, -1.2699999809265137, -1.2100000381469727, -0.4300000071525574, -1.5800000429153442, -1.149999976158142, -0.8100000023841858, -1.2400000095367432, 0.44999998807907104, -1.0700000524520874, -0.8399999737739563, -1.8799999952316284, -2.880000114440918, -0.019999999552965164, -1.2000000476837158, -2.6700000762939453] dt [1.6699999570846558, -2.190000057220459, -0.09000000357627869, 0.7200000286102295, -2.5299999713897705, -5.46999979019165, 1.7599999904632568, -0.4399999976158142, -0.3700000047683716, -0.8700000047683716, 1.409999966621399, -4.619999885559082, -2.7799999713897705, 1.1399999856948853, -5.03000020980835, -3.6600000858306885] gated_norm min/median/max [0.355 0.961 1.086]3 [('input_layernorm', -0.035, 0.355, 0.84, [46, 78, 30]), ('post_attention_layernorm', -0.204, 0.262, 0.463, [900, 838, 693])]  self_attn.q_norm.weight min/median/max [-0.211  0.391  1.227]  self_attn.k_norm.weight min/median/max [-0.898  0.385  1.633]4 [('input_layernorm', -0.01, 0.512, 0.84, [66, 74, 88]), ('post_attention_layernorm', -0.262, 0.252, 0.373, [818, 693, 900])]  recurrent A_log [-0.8799999952316284, -0.029999999329447746, -1.159999966621399, -0.6399999856948853, -0.8999999761581421, -2.5899999141693115, -1.350000023841858, -0.1599999964237213, 0.6000000238418579, -1.309999942779541, -0.9100000262260437, -0.6200000047683716, -2.9800000190734863, 0.5799999833106995, -1.3899999856948853, -2.7799999713897705] dt [0.03999999910593033, 2.25, -4.46999979019165, 1.8899999856948853, -5.380000114440918, -5.53000020980835, -5.28000020980835, 0.9300000071525574, 0.10999999940395355, -3.049999952316284, -0.25999999046325684, -5.5, -3.3399999141693115, -5.25, -0.11999999731779099, -4.03000020980835] gated_norm min/median/max [0.348 0.91  1.078]5 [('input_layernorm', -0.188, 0.473, 0.773, [66, 88, 42]), ('post_attention_layernorm', -0.355, 0.239, 0.365, [818, 481, 531])]  recurrent A_log [-0.27000001072883606, -2.109999895095825, -1.3700000047683716, -0.8700000047683716, -2.559999942779541, 0.6600000262260437, -0.07999999821186066, 2.0, -2.1600000858306885, -1.6699999570846558, -3.1700000762939453, -0.14000000059604645, -2.190000057220459, 0.23999999463558197, -1.590000033378601, -2.0799999237060547] dt [-0.12999999523162842, -3.859999895095825, -1.149999976158142, -5.190000057220459, -4.03000020980835, -6.5, -0.07000000029802322, -4.159999847412109, -3.559999942779541, -3.8399999141693115, -2.9700000286102295, -1.6200000047683716, -3.950000047683716, -0.800000011920929, 1.4199999570846558, -3.4800000190734863] gated_norm min/median/max [0.473 0.961 1.117]6 [('input_layernorm', -0.305, 0.428, 0.684, [88, 66, 74]), ('post_attention_layernorm', -0.391, 0.188, 0.293, [523, 443, 545])]  recurrent A_log [-1.7000000476837158, -0.30000001192092896, -1.190000057220459, -0.5899999737739563, -0.10000000149011612, -1.2999999523162842, -0.8700000047683716, -2.25, -1.1100000143051147, 0.44999998807907104, -0.3799999952316284, -0.6299999952316284, -0.4399999976158142, -0.47999998927116394, -0.5299999713897705, -2.450000047683716] dt [-1.0499999523162842, -1.340000033378601, -0.07000000029802322, -1.3300000429153442, -1.4800000190734863, -3.75, -5.96999979019165, -3.700000047683716, -3.119999885559082, -0.8399999737739563, -1.7200000286102295, 2.0299999713897705, -6.0, -1.7899999618530273, -0.05000000074505806, 2.0199999809265137] gated_norm min/median/max [0.531 1.    1.141]7 [('input_layernorm', -0.377, 0.146, 0.523, [46, 123, 545]), ('post_attention_layernorm', -0.447, 0.191, 0.309, [523, 481, 443])]  self_attn.q_norm.weight min/median/max [-0.239  0.5    1.023]  self_attn.k_norm.weight min/median/max [-0.165  0.492  1.062]8 [('input_layernorm', -0.422, 0.453, 0.648, [74, 66, 909]), ('post_attention_layernorm', -0.496, 0.175, 0.309, [523, 521, 443])]  recurrent A_log [-1.0499999523162842, -1.2599999904632568, -1.2100000381469727, -1.8899999856948853, -1.7999999523162842, -0.5, -1.4900000095367432, -0.9800000190734863, -0.14000000059604645, -2.299999952316284, -1.399999976158142, -1.0199999809265137, -0.9300000071525574, -2.7200000286102295, -0.41999998688697815, -1.0199999809265137] dt [-0.8799999952316284, -1.9800000190734863, -4.46999979019165, -3.7300000190734863, -3.0199999809265137, 0.20999999344348907, -4.21999979019165, -0.9100000262260437, -2.5, -4.5, -0.15000000596046448, 0.25, -4.690000057220459, -4.059999942779541, -1.1200000047683716, -0.20000000298023224] gated_norm min/median/max [0.617 1.016 1.156]9 [('input_layernorm', -0.445, 0.436, 0.664, [812, 951, 739]), ('post_attention_layernorm', -0.516, 0.154, 0.299, [523, 521, 443])]  recurrent A_log [-0.8999999761581421, -1.9900000095367432, -1.0800000429153442, -1.690000057220459, -1.2999999523162842, -2.5199999809265137, -0.7599999904632568, -1.909999966621399, -1.4500000476837158, 0.0, -2.450000047683716, -1.2100000381469727, -2.1700000762939453, -1.7100000381469727, -2.0199999809265137, -2.450000047683716] dt [-3.299999952316284, -3.9100000858306885, -1.899999976158142, -1.1699999570846558, -1.0499999523162842, -4.71999979019165, -1.100000023841858, -3.950000047683716, -2.390000104904175, -0.8100000023841858, 1.409999966621399, 1.6100000143051147, -4.21999979019165, -3.690000057220459, -4.21999979019165, -3.25] gated_norm min/median/max [0.711 1.031 1.195]10 [('input_layernorm', -0.494, 0.369, 0.645, [909, 816, 835]), ('post_attention_layernorm', -0.516, 0.123, 0.248, [523, 807, 443])]  recurrent A_log [-0.27000001072883606, 1.7599999904632568, -1.1699999570846558, -1.590000033378601, -0.5099999904632568, -1.0700000524520874, -1.1799999475479126, -1.0099999904632568, -1.090000033378601, -1.7100000381469727, -1.590000033378601, -1.590000033378601, -1.4500000476837158, -1.100000023841858, -1.4299999475479126, -1.8799999952316284] dt [-1.149999976158142, -7.0, 3.190000057220459, -1.5499999523162842, -1.75, -1.5199999809265137, -1.600000023841858, -0.3799999952316284, -1.8799999952316284, -2.5799999237060547, -3.75, -3.7699999809265137, -4.909999847412109, -0.9399999976158142, -1.7699999809265137, -1.2999999523162842] gated_norm min/median/max [0.824 1.047 1.156]11 [('input_layernorm', -0.605, 0.209, 0.699, [46, 523, 807]), ('post_attention_layernorm', -0.436, 0.151, 0.309, [523, 531, 807])]  self_attn.q_norm.weight min/median/max [-1.016  0.645  1.133]  self_attn.k_norm.weight min/median/max [-0.996  0.641  1.312]12 [('input_layernorm', -0.25, 0.551, 0.848, [74, 835, 816]), ('post_attention_layernorm', -0.322, 0.169, 0.338, [1004, 523, 816])]  recurrent A_log [-1.600000023841858, -0.5299999713897705, -2.2699999809265137, -1.2599999904632568, -1.3200000524520874, 0.0, -2.0299999713897705, -3.0299999713897705, -2.200000047683716, -0.7699999809265137, -1.7999999523162842, -0.05000000074505806, -1.7799999713897705, -2.0799999237060547, -2.049999952316284, -1.1100000143051147] dt [-5.0, -1.1699999570846558, -3.8399999141693115, -4.75, -1.9800000190734863, -6.659999847412109, -3.859999895095825, -3.4700000286102295, -4.590000152587891, -1.9199999570846558, -1.340000033378601, -0.5699999928474426, -4.96999979019165, -5.059999942779541, -1.2899999618530273, -1.2000000476837158] gated_norm min/median/max [0.746 1.008 1.141]13 [('input_layernorm', -0.264, 0.48, 0.836, [826, 66, 818]), ('post_attention_layernorm', -0.231, 0.179, 0.369, [1004, 1000, 927])]  recurrent A_log [-1.309999942779541, 0.14000000059604645, -0.6899999976158142, -1.4299999475479126, -1.090000033378601, -1.7000000476837158, -0.4399999976158142, -2.200000047683716, -0.9200000166893005, -1.5399999618530273, -1.0, -1.8300000429153442, -1.340000033378601, -0.8999999761581421, -1.409999966621399, -1.9900000095367432] dt [-1.4500000476837158, -0.5400000214576721, 0.3100000023841858, -1.659999966621399, -5.340000152587891, -2.6700000762939453, -1.149999976158142, 2.190000057220459, -0.6299999952316284, -0.009999999776482582, -1.7300000190734863, -2.2699999809265137, -0.8799999952316284, -3.0899999141693115, -3.119999885559082, -1.8799999952316284] gated_norm min/median/max [0.789 1.039 1.148]14 [('input_layernorm', -0.198, 0.424, 0.699, [74, 904, 909]), ('post_attention_layernorm', -0.096, 0.181, 0.34, [1004, 81, 162])]  rec[output cut at 10,000 characters]

python

# Hypothesis: full-attention copying of a previous self-check may sustain loops; inspect natural failures and the existing probe layout.for l in [15,18,19,22,23]:    p=f"model.language_model.layers.{l}."    if l%4!=3:        for suf in ["linear_attn.A_log","linear_attn.dt_bias"]:            z=load_original(p+suf).numpy()            print(l,suf,np.round(z,2).tolist())for fn in ["input_ids.npy","hidden_24.npy","mlp_act_23.npy","topk_ids.npy"]:    arr=np.load("/work/fwd_3/"+fn,mmap_mode="r")    print(fn,arr.shape,arr.dtype)print("gen1 index:",{k:v for k,v in json.load(open("/work/gen_1_acts/index.json")).items() if k!="samples"})for ss in [s for s in s4o if s["cut"]][:3]:    print("Failure",ss["id"])    for label,excerpt in [("start",ss["text"][:250]),("middle",ss["text"][7000:7250]),("tail",ss["text"][-250:])]:        print(label,excerpt.encode("ascii","replace").decode())print("Practice object:",type(practice).__name__, "by_id count:",len(by_id),"diagnostic count:",len(diag_ids))print("Notes update: first ablation was null; attention induction and distributed repetition directions remain untested.")

# Hypothesis: full-attention copying of a previous self-check may sustain loops; inspect natural failures and the existing probe layout.for l in [15,18,19,22,23]:    p=f"model.language_model.layers.{l}."    if l%4!=3:        for suf in ["linear_attn.A_log","linear_attn.dt_bias"]:            z=load_original(p+suf).numpy()            print(l,suf,np.round(z,2).tolist())for fn in ["input_ids.npy","hidden_24.npy","mlp_act_23.npy","topk_ids.npy"]:    arr=np.load("/work/fwd_3/"+fn,mmap_mode="r")    print(fn,arr.shape,arr.dtype)print("gen1 index:",{k:v for k,v in json.load(open("/work/gen_1_acts/index.json")).items() if k!="samples"})for ss in [s for s in s4o if s["cut"]][:3]:    print("Failure",ss["id"])    for label,excerpt in [("start",ss["text"][:250]),("middle",ss["text"][7000:7250]),("tail",ss["text"][-250:])]:        print(label,excerpt.encode("ascii","replace").decode())print("Practice object:",type(practice).__name__, "by_id count:",len(by_id),"diagnostic count:",len(diag_ids))print("Notes update: first ablation was null; attention induction and distributed repetition directions remain untested.")

OUTPUT of Python run 43 of at most 300:

18 linear_attn.A_log [-0.8799999952316284, -1.600000023841858, -1.2100000381469727, -1.309999942779541, -1.7599999904632568, -1.2000000476837158, -0.9300000071525574, -1.1200000047683716, -1.409999966621399, -1.7999999523162842, -1.4299999475479126, -1.559999942779541, -1.409999966621399, -0.9800000190734863, -1.6200000047683716, -1.5199999809265137]18 linear_attn.dt_bias [-5.340000152587891, 4.46999979019165, -5.340000152587891, 3.559999942779541, -1.5299999713897705, 0.6299999952316284, -1.2100000381469727, 4.440000057220459, -3.619999885559082, 2.0, -4.690000057220459, -4.119999885559082, -4.96999979019165, -4.28000020980835, -6.21999979019165, 1.8700000047683716]22 linear_attn.A_log [-1.690000057220459, -1.4800000190734863, -0.41999998688697815, -1.6699999570846558, -0.7699999809265137, -1.590000033378601, -1.559999942779541, -1.399999976158142, -1.409999966621399, -0.9800000190734863, -1.440000057220459, -1.0800000429153442, -1.6799999475479126, -0.8899999856948853, -1.5800000429153442, -1.2000000476837158]22 linear_attn.dt_bias [1.5, 1.340000033378601, 3.3399999141693115, -1.6200000047683716, 1.840000033378601, -3.8399999141693115, -2.880000114440918, 0.8899999856948853, 0.4300000071525574, 7.409999847412109, -2.8399999141693115, 4.190000057220459, -0.9599999785423279, -6.059999942779541, -0.6100000143051147, -5.840000152587891]input_ids.npy (2, 679) int64hidden_24.npy (2, 679, 1024) float16mlp_act_23.npy (2, 679, 3584) float16topk_ids.npy (2, 679, 10) int64gen1 index: {'layers': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24], 'mlp_layers': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23], 'files': {'hidden_last_prompt.npy': [256, 24, 1024], 'hidden_mean_answer.npy': [256, 24, 1024], 'mlp_last_prompt.npy': [256, 23, 3584], 'mlp_mean_answer.npy': [256, 23, 3584]}}Failure gsm8k_train_7354start Here's a thinking process that leads to the solution:
1.  **Analyze the Problem:**    *   **Goal:** Calculate the total gas needed for Helen cutting her lawn from March through October.    *   **Fuel Consumption Rate:** 2 gallons per 4th cut (evermiddle he start date is March and the end date is October.    If she cuts in March, she must have done so.    If she cuts in April, she must have done so.    If she cuts in September, she must have done so.    If she cuts in October, she must have done tail y, August.    Wait, I'm still confused.    Let's try to count the numbers again.    First list: March, April, September, October.    Second list: May, June, July, August.    Wait, I'm still confused.    Let's try to count the numbers again.   Failure gsm8k_train_7354start Here's a thinking process that leads to the solution:
1.  **Analyze the Request:**    *   **Goal:** Calculate total gas usage (gallons) from March through October.    *   **Input Data:**        *   Cutting frequency per month (in each month):   middle e the question is asking for the total gallons needed.                *   Let's try to calculate based on the "cost per cut" = 2 gallons.                *   Wait, the text says "uses 2 gallons of gas every 4th time she cuts her lawn".             tail   Wait, let's look at the numbers.                    *   2 gallons per 4th cut.                    *   Maybe it means: She pays 2 gallons for every *cut*.                    *   Wait, let's look at the phrasing "every 4th time she cuts her lawn".Failure gsm8k_train_4960start Here's my thought process for solving this problem:
1.  **Analyze the Request:**    *   **Characters:** Leticia, Scarlett, and Percy (a group of people).    *   **Event:** They decide to eat at a Greek restaurant for lunch.    *   **Prices:**   middle er receives the tip for the group, the total is 40.    *   Let's check if there's any other interpretation.    *   Could the question be a joke? "Leticia, Scarlett, and Percy" -> Leticia is a Greek goddess? No.    *   Could the prices be related ttail " are not the cost of the dishes, but the *unit prices*?    *   No, "prices for their dishes".    *   Let's check if the question is a trick because the "prices for their dishes" are not the cost of the dishes, but the *cost* of the dishes?    *  Practice object: dict by_id count: 15458 diagnostic count: 256Notes update: first ablation was null; attention induction and distributed repetition directions remain untested.

18 linear_attn.A_log [-0.8799999952316284, -1.600000023841858, -1.2100000381469727, -1.309999942779541, -1.7599999904632568, -1.2000000476837158, -0.9300000071525574, -1.1200000047683716, -1.409999966621399, -1.7999999523162842, -1.4299999475479126, -1.559999942779541, -1.409999966621399, -0.9800000190734863, -1.6200000047683716, -1.5199999809265137]18 linear_attn.dt_bias [-5.340000152587891, 4.46999979019165, -5.340000152587891, 3.559999942779541, -1.5299999713897705, 0.6299999952316284, -1.2100000381469727, 4.440000057220459, -3.619999885559082, 2.0, -4.690000057220459, -4.119999885559082, -4.96999979019165, -4.28000020980835, -6.21999979019165, 1.8700000047683716]22 linear_attn.A_log [-1.690000057220459, -1.4800000190734863, -0.41999998688697815, -1.6699999570846558, -0.7699999809265137, -1.590000033378601, -1.559999942779541, -1.399999976158142, -1.409999966621399, -0.9800000190734863, -1.440000057220459, -1.0800000429153442, -1.6799999475479126, -0.8899999856948853, -1.5800000429153442, -1.2000000476837158]22 linear_attn.dt_bias [1.5, 1.340000033378601, 3.3399999141693115, -1.6200000047683716, 1.840000033378601, -3.8399999141693115, -2.880000114440918, 0.8899999856948853, 0.4300000071525574, 7.409999847412109, -2.8399999141693115, 4.190000057220459, -0.9599999785423279, -6.059999942779541, -0.6100000143051147, -5.840000152587891]input_ids.npy (2, 679) int64hidden_24.npy (2, 679, 1024) float16mlp_act_23.npy (2, 679, 3584) float16topk_ids.npy (2, 679, 10) int64gen1 index: {'layers': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24], 'mlp_layers': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23], 'files': {'hidden_last_prompt.npy': [256, 24, 1024], 'hidden_mean_answer.npy': [256, 24, 1024], 'mlp_last_prompt.npy': [256, 23, 3584], 'mlp_mean_answer.npy': [256, 23, 3584]}}Failure gsm8k_train_7354start Here's a thinking process that leads to the solution:
1.  **Analyze the Problem:**    *   **Goal:** Calculate the total gas needed for Helen cutting her lawn from March through October.    *   **Fuel Consumption Rate:** 2 gallons per 4th cut (evermiddle he start date is March and the end date is October.    If she cuts in March, she must have done so.    If she cuts in April, she must have done so.    If she cuts in September, she must have done so.    If she cuts in October, she must have done tail y, August.    Wait, I'm still confused.    Let's try to count the numbers again.    First list: March, April, September, October.    Second list: May, June, July, August.    Wait, I'm still confused.    Let's try to count the numbers again.   Failure gsm8k_train_7354start Here's a thinking process that leads to the solution:
1.  **Analyze the Request:**    *   **Goal:** Calculate total gas usage (gallons) from March through October.    *   **Input Data:**        *   Cutting frequency per month (in each month):   middle e the question is asking for the total gallons needed.                *   Let's try to calculate based on the "cost per cut" = 2 gallons.                *   Wait, the text says "uses 2 gallons of gas every 4th time she cuts her lawn".             tail   Wait, let's look at the numbers.                    *   2 gallons per 4th cut.                    *   Maybe it means: She pays 2 gallons for every *cut*.                    *   Wait, let's look at the phrasing "every 4th time she cuts her lawn".Failure gsm8k_train_4960start Here's my thought process for solving this problem:
1.  **Analyze the Request:**    *   **Characters:** Leticia, Scarlett, and Percy (a group of people).    *   **Event:** They decide to eat at a Greek restaurant for lunch.    *   **Prices:**   middle er receives the tip for the group, the total is 40.    *   Let's check if there's any other interpretation.    *   Could the question be a joke? "Leticia, Scarlett, and Percy" -> Leticia is a Greek goddess? No.    *   Could the prices be related ttail " are not the cost of the dishes, but the *unit prices*?    *   No, "prices for their dishes".    *   Let's check if the question is a trick because the "prices for their dishes" are not the cost of the dishes, but the *cost* of the dishes?    *  Practice object: dict by_id count: 15458 diagnostic count: 256Notes update: first ablation was null; attention induction and distributed repetition directions remain untested.

forward

{"type":"teacher_forced","model":"original","texts":["<|im_start|>user\nJames has 5 dozen boxes of matches. Each box contains 20 matches. How many matches does James have in total?\nPlease reason step by step, and put your final answer within \\boxed{}.<|im_end|>\n<|im_start|>assistant\n<think>\nHere's my thought process for solving this problem:\n\n1. One dozen is 12, so five dozen boxes means 5 times 12 = 60 boxes.\n2. Each box contains 20 matches, so the total is 60 times 20 = 1200 matches.\n3. To check another way, one dozen boxes contains 12 times 20 = 240 matches. Five such groups contain 5 times 240 = 1200 matches. The two calculations agree.\n\nWait, is it possible that \"20 matches\" refers to the total rather than the number in each box? No, the problem says each box contains 20 matches. There are 60 boxes, so there are 1200 matches.\nWait, is it possible that \"20 matches\" refers to the total rather than the number in each box? No, the problem says each box contains 20 matches. There are 60 boxes, so there are 1200 matches.\nWait, is it possible that \"20 matches\" refers to the total rather than the number in each box? No, the problem says each box contains 20 matches. There are 60 boxes, so there are 1200 matches.\nWait, is it possible that \"20 matches\" refers to the total rather than the number in each box? No, the problem says each box contains 20 matches. There are 60 boxes, so there are 1200 matches.\nWait, is it possible that \"20 matches\" refers to the total rather than the number in each box? No, the problem says each box contains 20 matches. There are 60 boxes, so there are 1200 matches.\nWait, is it possible that \"20 matches\" refers to the total rather than the number in each box? No, the problem says each box contains 20 matches. There are 60 boxes, so there are 1200 matches.\n\nA unit check gives boxes times matches per box, leaving matches. This resolves the interpretation and confirms the arithmetic.\n\nWait, let's check the calculation again. Five times twelve is sixty. Sixty times twenty is twelve hundred. The result is 1200 matches.\nWait, let's check the calculation again. Five times twelve is sixty. Sixty times twenty is twelve hundred. The result is 1200 matches.\nWait, let's check the calculation again. Five times twelve is sixty. Sixty times twenty is twelve hundred. The result is 1200 matches.\nWait, let's check the calculation again. Five times twelve is sixty. Sixty times twenty is twelve hundred. The result is 1200 matches.\nWait, let's check the calculation again. Five times twelve is sixty. Sixty times twenty is twelve hundred. The result is 1200 matches.\nWait, let's check the calculation again. Five times twelve is sixty. Sixty times twenty is twelve hundred. The result is 1200 matches.\n\nThe calculation and units agree, so the answer is established.\n</think>\nFive dozen is 60 boxes, and 60 times 20 is 1200. Thus the answer is \\boxed{1200}.<|im_end|>","<|im_start|>user\nSolve the equation x^2 - 5x + 6 = 0 for real x.\nPlease reason step by step, and put your final answer within \\boxed{}.<|im_end|>\n<|im_start|>assistant\n<think>\nI need two numbers whose product is 6 and whose sum is -5. Those numbers are -2 and -3. Therefore x^2 - 5x + 6 = (x - 2)(x - 3). By the zero product property, x = 2 or x = 3.\nChecking x = 2 gives 4 - 10 + 6 = 0. Checking x = 3 gives 9 - 15 + 6 = 0. Both solutions satisfy the original equation.\n\nWait, why did I think the solutions are positive? The factors are x minus 2 and x minus 3. Setting the first factor to zero gives x = 2. Setting the second factor to zero gives x = 3. Yes, the solutions are positive.\nWait, why did I think the solutions are positive? The factors are x minus 2 and x minus 3. Setting the first factor to zero gives x = 2. Setting the second factor to zero gives x = 3. Yes, the solutions are positive.\nWait, why did I think the solutions are positive? The factors are x minus 2 and x minus 3. Setting the first factor to zero gives x = 2. Setting the second factor to zero gives x = 3. Yes, the solutions are positive.\nWait, why did I think the solutions are positive? The factors are x minus 2 and x minus 3. Setting the first factor to zero gives x = 2. Setting the second factor to zero gives x = 3. Yes, the solutions are positive.\nWait, why did I think the solutions are positive? The factors are x minus 2 and x minus 3. Setting the first factor to zero gives x = 2. Setting the second factor to zero gives x = 3. Yes, the solutions are positive.\nWait, why did I think the solutions are positive? The factors are x minus 2 and x minus 3. Setting the first factor to zero gives x = 2. Setting the second factor to zero gives x = 3. Yes, the solutions are positive.\n\nAn independent method is the quadratic formula. The discriminant is 25 minus 24, which is 1. Thus x = (5 plus or minus 1)/2, giving 3 and 2. This agrees with factoring.\n\nLet's check if the question is a trick because the numbers in the factors have minus signs. No, x - 2 = 0 means x = 2, and x - 3 = 0 means x = 3.\nLet's check if the question is a trick because the numbers in the factors have minus signs. No, x - 2 = 0 means x = 2, and x - 3 = 0 means x = 3.\nLet's check if the question is a trick because the numbers in the factors have minus signs. No, x - 2 = 0 means x = 2, and x - 3 = 0 means x = 3.\nLet's check if the question is a trick because the numbers in the factors have minus signs. No, x - 2 = 0 means x = 2, and x - 3 = 0 means x = 3.\nLet's check if the question is a trick because the numbers in the factors have minus signs. No, x - 2 = 0 means x = 2, and x - 3 = 0 means x = 3.\nLet's check if the question is a trick because the numbers in the factors have minus signs. No, x - 2 = 0 means x = 2, and x - 3 = 0 means x = 3.\n\nThere are exactly two roots because this is a quadratic with nonzero discriminant. The verification is complete.\n</think>\nFactoring gives (x - 2)(x - 3) = 0, so \\boxed{x \\in \\{2,3\\}}.<|im_end|>"],"return":{"token_logprobs":true,"topk":20,"hidden_states":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24],"mlp_act":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23],"attn_layers":[3,7,11,15,19,23]}}

{"type":"teacher_forced","model":"original","texts":["<|im_start|>user\nJames has 5 dozen boxes of matches. Each box contains 20 matches. How many matches does James have in total?\nPlease reason step by step, and put your final answer within \\boxed{}.<|im_end|>\n<|im_start|>assistant\n<think>\nHere's my thought process for solving this problem:\n\n1. One dozen is 12, so five dozen boxes means 5 times 12 = 60 boxes.\n2. Each box contains 20 matches, so the total is 60 times 20 = 1200 matches.\n3. To check another way, one dozen boxes contains 12 times 20 = 240 matches. Five such groups contain 5 times 240 = 1200 matches. The two calculations agree.\n\nWait, is it possible that \"20 matches\" refers to the total rather than the number in each box? No, the problem says each box contains 20 matches. There are 60 boxes, so there are 1200 matches.\nWait, is it possible that \"20 matches\" refers to the total rather than the number in each box? No, the problem says each box contains 20 matches. There are 60 boxes, so there are 1200 matches.\nWait, is it possible that \"20 matches\" refers to the total rather than the number in each box? No, the problem says each box contains 20 matches. There are 60 boxes, so there are 1200 matches.\nWait, is it possible that \"20 matches\" refers to the total rather than the number in each box? No, the problem says each box contains 20 matches. There are 60 boxes, so there are 1200 matches.\nWait, is it possible that \"20 matches\" refers to the total rather than the number in each box? No, the problem says each box contains 20 matches. There are 60 boxes, so there are 1200 matches.\nWait, is it possible that \"20 matches\" refers to the total rather than the number in each box? No, the problem says each box contains 20 matches. There are 60 boxes, so there are 1200 matches.\n\nA unit check gives boxes times matches per box, leaving matches. This resolves the interpretation and confirms the arithmetic.\n\nWait, let's check the calculation again. Five times twelve is sixty. Sixty times twenty is twelve hundred. The result is 1200 matches.\nWait, let's check the calculation again. Five times twelve is sixty. Sixty times twenty is twelve hundred. The result is 1200 matches.\nWait, let's check the calculation again. Five times twelve is sixty. Sixty times twenty is twelve hundred. The result is 1200 matches.\nWait, let's check the calculation again. Five times twelve is sixty. Sixty times twenty is twelve hundred. The result is 1200 matches.\nWait, let's check the calculation again. Five times twelve is sixty. Sixty times twenty is twelve hundred. The result is 1200 matches.\nWait, let's check the calculation again. Five times twelve is sixty. Sixty times twenty is twelve hundred. The result is 1200 matches.\n\nThe calculation and units agree, so the answer is established.\n</think>\nFive dozen is 60 boxes, and 60 times 20 is 1200. Thus the answer is \\boxed{1200}.<|im_end|>","<|im_start|>user\nSolve the equation x^2 - 5x + 6 = 0 for real x.\nPlease reason step by step, and put your final answer within \\boxed{}.<|im_end|>\n<|im_start|>assistant\n<think>\nI need two numbers whose product is 6 and whose sum is -5. Those numbers are -2 and -3. Therefore x^2 - 5x + 6 = (x - 2)(x - 3). By the zero product property, x = 2 or x = 3.\nChecking x = 2 gives 4 - 10 + 6 = 0. Checking x = 3 gives 9 - 15 + 6 = 0. Both solutions satisfy the original equation.\n\nWait, why did I think the solutions are positive? The factors are x minus 2 and x minus 3. Setting the first factor to zero gives x = 2. Setting the second factor to zero gives x = 3. Yes, the solutions are positive.\nWait, why did I think the solutions are positive? The factors are x minus 2 and x minus 3. Setting the first factor to zero gives x = 2. Setting the second factor to zero gives x = 3. Yes, the solutions are positive.\nWait, why did I think the solutions are positive? The factors are x minus 2 and x minus 3. Setting the first factor to zero gives x = 2. Setting the second factor to zero gives x = 3. Yes, the solutions are positive.\nWait, why did I think the solutions are positive? The factors are x minus 2 and x minus 3. Setting the first factor to zero gives x = 2. Setting the second factor to zero gives x = 3. Yes, the solutions are positive.\nWait, why did I think the solutions are positive? The factors are x minus 2 and x minus 3. Setting the first factor to zero gives x = 2. Setting the second factor to zero gives x = 3. Yes, the solutions are positive.\nWait, why did I think the solutions are positive? The factors are x minus 2 and x minus 3. Setting the first factor to zero gives x = 2. Setting the second factor to zero gives x = 3. Yes, the solutions are positive.\n\nAn independent method is the quadratic formula. The discriminant is 25 minus 24, which is 1. Thus x = (5 plus or minus 1)/2, giving 3 and 2. This agrees with factoring.\n\nLet's check if the question is a trick because the numbers in the factors have minus signs. No, x - 2 = 0 means x = 2, and x - 3 = 0 means x = 3.\nLet's check if the question is a trick because the numbers in the factors have minus signs. No, x - 2 = 0 means x = 2, and x - 3 = 0 means x = 3.\nLet's check if the question is a trick because the numbers in the factors have minus signs. No, x - 2 = 0 means x = 2, and x - 3 = 0 means x = 3.\nLet's check if the question is a trick because the numbers in the factors have minus signs. No, x - 2 = 0 means x = 2, and x - 3 = 0 means x = 3.\nLet's check if the question is a trick because the numbers in the factors have minus signs. No, x - 2 = 0 means x = 2, and x - 3 = 0 means x = 3.\nLet's check if the question is a trick because the numbers in the factors have minus signs. No, x - 2 = 0 means x = 2, and x - 3 = 0 means x = 3.\n\nThere are exactly two roots because this is a quadratic with nonzero discriminant. The verification is complete.\n</think>\nFactoring gives (x - 2)(x - 3) = 0, so \\boxed{x \\in \\{2,3\\}}.<|im_end|>"],"return":{"token_logprobs":true,"topk":20,"hidden_states":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24],"mlp_act":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23],"attn_layers":[3,7,11,15,19,23]}}

Forward pass 4 of 10 done (teacher_forced); results in /work/fwd_5/. Summary:

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Forward passes left: 6.

python

# Hypothesis: a small number of induction heads attend to the next token in previous self-checks, creating a measurable copy-feedback loop.meta5=json.load(open("/work/fwd_5/meta.json"))texts5=meta5["texts"]ids5=np.load("/work/fwd_5/input_ids.npy")starts5=[["Wait, is it possible","Wait, let's check"],["Wait, why did I think","Let's check if the question"]]groups5=[]for ti,text5 in enumerate(texts5):    enc=tok.encode(text5)    offsets=enc.offsets    for phrase in starts5[ti]:        chars=[]; pos=0        while True:            pos=text5.find(phrase,pos)            if pos<0: break            chars.append(pos);pos+=len(phrase)        spans=[]        for start in chars:            stop=text5.index("\n",start)            inds=[j for j,(a,b) in enumerate(offsets) if b>start and a<stop]            spans.append(np.array(inds))        lengths=[len(x) for x in spans]        assert len(set(lengths))==1        assert all(np.array_equal(ids5[ti,spans[0][2:-2]],ids5[ti,s[2:-2]]) for s in spans)        groups5.append((ti,spans))        print("Probe group",ti,phrase,"occurrences",len(spans),"tokens each",lengths[0],"starts",[int(s[0]) for s in spans])head_scores=[]for l in [3,7,11,15,19,23]:    att=np.load(f"/work/fwd_5/attn_{l}.npy",mmap_mode="r")    for h in range(8):        shifted={d:[] for d in [-1,0,1,2]}        total=[]        for ti,spans in groups5:            for ri in range(2,len(spans)):                qs=spans[ri][3:-3]                for shift in shifted:                    pp=[att[ti,h,qs,spans[p][3+shift:len(spans[p])-3+shift]].astype(float) for p in range(ri)]                    shifted[shift].extend(np.sum(pp,axis=0))                past=np.concatenate(spans[:ri])                total.extend(np.asarray(att[ti,h])[np.ix_(qs,past)].sum(axis=1).tolist())        head_scores.append(dict(layer=l,head=h,induction=round(float(np.mean(shifted[1])),4),                                same=round(float(np.mean(shifted[0])),4),                                previous=round(float(np.mean(shifted[-1])),4),                                plus2=round(float(np.mean(shifted[2])),4),                                past_block=round(float(np.mean(total)),4)))print("Highest induction heads:")for r in sorted(head_scores,key=lambda r:r["induction"],reverse=True)[:20]: print(r)print("Matched hidden repetition drift:")rep_dirs5={}for l in range(1,25):    H=np.load(f"/work/fwd_5/hidden_{l}.npy",mmap_mode="r")    diffs=[np.mean(H[ti,np.concatenate(spans[-3:])].astype(np.float32),axis=0)-np.mean(H[ti,spans[0]].astype(np.float32),axis=0) for ti,spans in groups5]    u=np.mean(diffs,axis=0); rep_dirs5[l]=u    cos=np.mean([np.dot(d,u)/(np.linalg.norm(d)*np.linalg.norm(u)) for d in diffs])    print(l,"drift_norm",round(float(np.linalg.norm(u)),3),"consistency",round(float(cos),3))

# Hypothesis: a small number of induction heads attend to the next token in previous self-checks, creating a measurable copy-feedback loop.meta5=json.load(open("/work/fwd_5/meta.json"))texts5=meta5["texts"]ids5=np.load("/work/fwd_5/input_ids.npy")starts5=[["Wait, is it possible","Wait, let's check"],["Wait, why did I think","Let's check if the question"]]groups5=[]for ti,text5 in enumerate(texts5):    enc=tok.encode(text5)    offsets=enc.offsets    for phrase in starts5[ti]:        chars=[]; pos=0        while True:            pos=text5.find(phrase,pos)            if pos<0: break            chars.append(pos);pos+=len(phrase)        spans=[]        for start in chars:            stop=text5.index("\n",start)            inds=[j for j,(a,b) in enumerate(offsets) if b>start and a<stop]            spans.append(np.array(inds))        lengths=[len(x) for x in spans]        assert len(set(lengths))==1        assert all(np.array_equal(ids5[ti,spans[0][2:-2]],ids5[ti,s[2:-2]]) for s in spans)        groups5.append((ti,spans))        print("Probe group",ti,phrase,"occurrences",len(spans),"tokens each",lengths[0],"starts",[int(s[0]) for s in spans])head_scores=[]for l in [3,7,11,15,19,23]:    att=np.load(f"/work/fwd_5/attn_{l}.npy",mmap_mode="r")    for h in range(8):        shifted={d:[] for d in [-1,0,1,2]}        total=[]        for ti,spans in groups5:            for ri in range(2,len(spans)):                qs=spans[ri][3:-3]                for shift in shifted:                    pp=[att[ti,h,qs,spans[p][3+shift:len(spans[p])-3+shift]].astype(float) for p in range(ri)]                    shifted[shift].extend(np.sum(pp,axis=0))                past=np.concatenate(spans[:ri])                total.extend(np.asarray(att[ti,h])[np.ix_(qs,past)].sum(axis=1).tolist())        head_scores.append(dict(layer=l,head=h,induction=round(float(np.mean(shifted[1])),4),                                same=round(float(np.mean(shifted[0])),4),                                previous=round(float(np.mean(shifted[-1])),4),                                plus2=round(float(np.mean(shifted[2])),4),                                past_block=round(float(np.mean(total)),4)))print("Highest induction heads:")for r in sorted(head_scores,key=lambda r:r["induction"],reverse=True)[:20]: print(r)print("Matched hidden repetition drift:")rep_dirs5={}for l in range(1,25):    H=np.load(f"/work/fwd_5/hidden_{l}.npy",mmap_mode="r")    diffs=[np.mean(H[ti,np.concatenate(spans[-3:])].astype(np.float32),axis=0)-np.mean(H[ti,spans[0]].astype(np.float32),axis=0) for ti,spans in groups5]    u=np.mean(diffs,axis=0); rep_dirs5[l]=u    cos=np.mean([np.dot(d,u)/(np.linalg.norm(d)*np.linalg.norm(u)) for d in diffs])    print(l,"drift_norm",round(float(np.linalg.norm(u)),3),"consistency",round(float(cos),3))

OUTPUT of Python run 44 of at most 300:

Probe group 0 Wait, is it possible occurrences 6 tokens each 53 starts [170, 224, 278, 332, 386, 440]Probe group 0 Wait, let's check occurrences 6 tokens each 33 starts [517, 551, 585, 619, 653, 687]Probe group 1 Wait, why did I think occurrences 6 tokens each 55 starts [158, 214, 270, 326, 382, 438]Probe group 1 Let's check if the question occurrences 6 tokens each 48 starts [546, 595, 644, 693, 742, 791]Highest induction heads:{'layer': 15, 'head': 7, 'induction': 0.8947, 'same': 0.0133, 'previous': 0.0018, 'plus2': 0.0545, 'past_block': 0.9866}{'layer': 11, 'head': 1, 'induction': 0.7381, 'same': 0.0115, 'previous': 0.0018, 'plus2': 0.0759, 'past_block': 0.896}{'layer': 23, 'head': 5, 'induction': 0.5919, 'same': 0.0414, 'previous': 0.0112, 'plus2': 0.0323, 'past_block': 0.8124}{'layer': 23, 'head': 0, 'induction': 0.4614, 'same': 0.029, 'previous': 0.014, 'plus2': 0.062, 'past_block': 0.7523}{'layer': 19, 'head': 5, 'induction': 0.4174, 'same': 0.0416, 'previous': 0.0134, 'plus2': 0.1034, 'past_block': 0.7738}{'layer': 19, 'head': 6, 'induction': 0.3394, 'same': 0.0147, 'previous': 0.0078, 'plus2': 0.1602, 'past_block': 0.7365}{'layer': 23, 'head': 7, 'induction': 0.3383, 'same': 0.0313, 'previous': 0.0155, 'plus2': 0.0714, 'past_block': 0.6718}{'layer': 19, 'head': 7, 'induction': 0.3215, 'same': 0.0146, 'previous': 0.0093, 'plus2': 0.1532, 'past_block': 0.6926}{'layer': 15, 'head': 5, 'induction': 0.3151, 'same': 0.0248, 'previous': 0.0084, 'plus2': 0.3022, 'past_block': 0.829}{'layer': 23, 'head': 4, 'induction': 0.2581, 'same': 0.0232, 'previous': 0.0111, 'plus2': 0.0621, 'past_block': 0.5494}{'layer': 19, 'head': 2, 'induction': 0.2363, 'same': 0.0239, 'previous': 0.0114, 'plus2': 0.1112, 'past_block': 0.6026}{'layer': 19, 'head': 0, 'induction': 0.2324, 'same': 0.0361, 'previous': 0.0176, 'plus2': 0.1018, 'past_block': 0.6072}{'layer': 23, 'head': 1, 'induction': 0.2321, 'same': 0.0167, 'previous': 0.0118, 'plus2': 0.0559, 'past_block': 0.555}{'layer': 23, 'head': 3, 'induction': 0.2243, 'same': 0.0102, 'previous': 0.0069, 'plus2': 0.0921, 'past_block': 0.5404}{'layer': 15, 'head': 6, 'induction': 0.2134, 'same': 0.0172, 'previous': 0.0065, 'plus2': 0.3733, 'past_block': 0.9341}{'layer': 3, 'head': 5, 'induction': 0.1974, 'same': 0.089, 'previous': 0.0155, 'plus2': 0.074, 'past_block': 0.7663}{'layer': 15, 'head': 4, 'induction': 0.1786, 'same': 0.0209, 'previous': 0.0089, 'plus2': 0.2166, 'past_block': 0.7365}{'layer': 15, 'head': 2, 'induction': 0.1755, 'same': 0.0323, 'previous': 0.0126, 'plus2': 0.1798, 'past_block': 0.8551}{'layer': 19, 'head': 3, 'induction': 0.1732, 'same': 0.0229, 'previous': 0.0107, 'plus2': 0.0798, 'past_block': 0.5108}{'layer': 15, 'head': 3, 'induction': 0.1667, 'same': 0.0351, 'previous': 0.0131, 'plus2': 0.115, 'past_block': 0.6848}Matched hidden repetition drift:1 drift_norm 0.099 consistency 0.9762 drift_norm 0.213 consistency 0.9753 drift_norm 0.31 consistency 0.9734 drift_norm 0.403 consistency 0.9675 drift_norm 0.442 consistency 0.9656 drift_norm 0.476 consistency 0.9627 drift_norm 0.557 consistency 0.9428 drift_norm 0.593 consistency 0.959 drift_norm 0.626 consistency 0.95510 drift_norm 0.713 consistency 0.95611 drift_norm 0.91 consistency 0.95712 drift_norm 1.067 consistency 0.96413 drift_norm 1.203 consistency 0.96414 drift_norm 1.444 consistency 0.96115 drift_norm 1.661 consistency 0.95516 drift_norm 2.007 consistency 0.95317 drift_norm 2.318 consistency 0.9518 drift_norm 2.58 consistency 0.95319 drift_norm 2.651 consistency 0.94920 drift_norm 3.259 consistency 0.95321 drift_norm 3.397 consistency 0.94522 drift_norm 3.661 consistency 0.93923 drift_norm 3.66 consistency 0.92724 drift_norm 45.188 consistency 0.892

Probe group 0 Wait, is it possible occurrences 6 tokens each 53 starts [170, 224, 278, 332, 386, 440]Probe group 0 Wait, let's check occurrences 6 tokens each 33 starts [517, 551, 585, 619, 653, 687]Probe group 1 Wait, why did I think occurrences 6 tokens each 55 starts [158, 214, 270, 326, 382, 438]Probe group 1 Let's check if the question occurrences 6 tokens each 48 starts [546, 595, 644, 693, 742, 791]Highest induction heads:{'layer': 15, 'head': 7, 'induction': 0.8947, 'same': 0.0133, 'previous': 0.0018, 'plus2': 0.0545, 'past_block': 0.9866}{'layer': 11, 'head': 1, 'induction': 0.7381, 'same': 0.0115, 'previous': 0.0018, 'plus2': 0.0759, 'past_block': 0.896}{'layer': 23, 'head': 5, 'induction': 0.5919, 'same': 0.0414, 'previous': 0.0112, 'plus2': 0.0323, 'past_block': 0.8124}{'layer': 23, 'head': 0, 'induction': 0.4614, 'same': 0.029, 'previous': 0.014, 'plus2': 0.062, 'past_block': 0.7523}{'layer': 19, 'head': 5, 'induction': 0.4174, 'same': 0.0416, 'previous': 0.0134, 'plus2': 0.1034, 'past_block': 0.7738}{'layer': 19, 'head': 6, 'induction': 0.3394, 'same': 0.0147, 'previous': 0.0078, 'plus2': 0.1602, 'past_block': 0.7365}{'layer': 23, 'head': 7, 'induction': 0.3383, 'same': 0.0313, 'previous': 0.0155, 'plus2': 0.0714, 'past_block': 0.6718}{'layer': 19, 'head': 7, 'induction': 0.3215, 'same': 0.0146, 'previous': 0.0093, 'plus2': 0.1532, 'past_block': 0.6926}{'layer': 15, 'head': 5, 'induction': 0.3151, 'same': 0.0248, 'previous': 0.0084, 'plus2': 0.3022, 'past_block': 0.829}{'layer': 23, 'head': 4, 'induction': 0.2581, 'same': 0.0232, 'previous': 0.0111, 'plus2': 0.0621, 'past_block': 0.5494}{'layer': 19, 'head': 2, 'induction': 0.2363, 'same': 0.0239, 'previous': 0.0114, 'plus2': 0.1112, 'past_block': 0.6026}{'layer': 19, 'head': 0, 'induction': 0.2324, 'same': 0.0361, 'previous': 0.0176, 'plus2': 0.1018, 'past_block': 0.6072}{'layer': 23, 'head': 1, 'induction': 0.2321, 'same': 0.0167, 'previous': 0.0118, 'plus2': 0.0559, 'past_block': 0.555}{'layer': 23, 'head': 3, 'induction': 0.2243, 'same': 0.0102, 'previous': 0.0069, 'plus2': 0.0921, 'past_block': 0.5404}{'layer': 15, 'head': 6, 'induction': 0.2134, 'same': 0.0172, 'previous': 0.0065, 'plus2': 0.3733, 'past_block': 0.9341}{'layer': 3, 'head': 5, 'induction': 0.1974, 'same': 0.089, 'previous': 0.0155, 'plus2': 0.074, 'past_block': 0.7663}{'layer': 15, 'head': 4, 'induction': 0.1786, 'same': 0.0209, 'previous': 0.0089, 'plus2': 0.2166, 'past_block': 0.7365}{'layer': 15, 'head': 2, 'induction': 0.1755, 'same': 0.0323, 'previous': 0.0126, 'plus2': 0.1798, 'past_block': 0.8551}{'layer': 19, 'head': 3, 'induction': 0.1732, 'same': 0.0229, 'previous': 0.0107, 'plus2': 0.0798, 'past_block': 0.5108}{'layer': 15, 'head': 3, 'induction': 0.1667, 'same': 0.0351, 'previous': 0.0131, 'plus2': 0.115, 'past_block': 0.6848}Matched hidden repetition drift:1 drift_norm 0.099 consistency 0.9762 drift_norm 0.213 consistency 0.9753 drift_norm 0.31 consistency 0.9734 drift_norm 0.403 consistency 0.9675 drift_norm 0.442 consistency 0.9656 drift_norm 0.476 consistency 0.9627 drift_norm 0.557 consistency 0.9428 drift_norm 0.593 consistency 0.959 drift_norm 0.626 consistency 0.95510 drift_norm 0.713 consistency 0.95611 drift_norm 0.91 consistency 0.95712 drift_norm 1.067 consistency 0.96413 drift_norm 1.203 consistency 0.96414 drift_norm 1.444 consistency 0.96115 drift_norm 1.661 consistency 0.95516 drift_norm 2.007 consistency 0.95317 drift_norm 2.318 consistency 0.9518 drift_norm 2.58 consistency 0.95319 drift_norm 2.651 consistency 0.94920 drift_norm 3.259 consistency 0.95321 drift_norm 3.397 consistency 0.94522 drift_norm 3.661 consistency 0.93923 drift_norm 3.66 consistency 0.92724 drift_norm 45.188 consistency 0.892

python

# Hypothesis: weakening the two strongest next-token copying heads will reduce redundant self-check loops without directly encouraging thinking termination.copy_heads={11:[1],15:[7]}def apply_copy_strength(strength, heads=None):    heads=copy_heads if heads is None else heads    for name in list(edited()): drop_edit(name)    result=[]    for l,hs in heads.items():        name=f"model.language_model.layers.{l}.self_attn.o_proj.weight"        w0=load_original(name); w=w0.clone()        assert w.shape==(1024,2048)        for h in hs: w[:,h*256:(h+1)*256]*=(1-strength)        save_edit(name,w)        result.append((l,hs,strength,round(float((w-w0).norm()/w0.norm()),4)))    return resultprint("Edit:",apply_copy_strength(0.5))base_records=[json.loads(s) for s in open("/work/gen_1.jsonl")]rng=np.random.default_rng(635)dose_ids=[]for src in ["gsm8k","math"]:    available=[r["id"] for r in base_records if r["source"]==src]    dose_ids.extend(rng.choice(available,64,replace=False).tolist())print("Dose-test IDs:",json.dumps(dose_ids))with open("/work/notes.md","a") as f:    f.write("\nPass 4 (fwd_5), properly templated matched self-checks, two math contexts/four distinct repeated phrases: induction attention to corresponding NEXT token in previous copies: L15H7=.8947, L11H1=.7381; next best L23H5=.5919. Same-position attention only .0133/.0115. Hypothesis: excessive next-token-copy feedback. Testing 50% attenuation of o_proj head blocks L11H1 and L15H7, no EOS/thinking edits. Six passes remain before test.\n")

# Hypothesis: weakening the two strongest next-token copying heads will reduce redundant self-check loops without directly encouraging thinking termination.copy_heads={11:[1],15:[7]}def apply_copy_strength(strength, heads=None):    heads=copy_heads if heads is None else heads    for name in list(edited()): drop_edit(name)    result=[]    for l,hs in heads.items():        name=f"model.language_model.layers.{l}.self_attn.o_proj.weight"        w0=load_original(name); w=w0.clone()        assert w.shape==(1024,2048)        for h in hs: w[:,h*256:(h+1)*256]*=(1-strength)        save_edit(name,w)        result.append((l,hs,strength,round(float((w-w0).norm()/w0.norm()),4)))    return resultprint("Edit:",apply_copy_strength(0.5))base_records=[json.loads(s) for s in open("/work/gen_1.jsonl")]rng=np.random.default_rng(635)dose_ids=[]for src in ["gsm8k","math"]:    available=[r["id"] for r in base_records if r["source"]==src]    dose_ids.extend(rng.choice(available,64,replace=False).tolist())print("Dose-test IDs:",json.dumps(dose_ids))with open("/work/notes.md","a") as f:    f.write("\nPass 4 (fwd_5), properly templated matched self-checks, two math contexts/four distinct repeated phrases: induction attention to corresponding NEXT token in previous copies: L15H7=.8947, L11H1=.7381; next best L23H5=.5919. Same-position attention only .0133/.0115. Hypothesis: excessive next-token-copy feedback. Testing 50% attenuation of o_proj head blocks L11H1 and L15H7, no EOS/thinking edits. Six passes remain before test.\n")

OUTPUT of Python run 45 of at most 300:

Edit: [(11, [1], 0.5, 0.1523), (15, [7], 0.5, 0.1616)]Dose-test IDs: ["gsm8k_train_4044", "gsm8k_train_5132", "gsm8k_train_1101", "gsm8k_train_7182", "gsm8k_train_1927", "gsm8k_train_5327", "gsm8k_train_700", "gsm8k_train_7181", "gsm8k_train_6980", "gsm8k_train_5313", "gsm8k_train_506", "gsm8k_train_1853", "gsm8k_train_1201", "gsm8k_train_1028", "gsm8k_train_4960", "gsm8k_train_3835", "gsm8k_train_1547", "gsm8k_train_2079", "gsm8k_train_2159", "gsm8k_train_5039", "gsm8k_train_7435", "gsm8k_train_48", "gsm8k_train_5840", "gsm8k_train_3284", "gsm8k_train_76", "gsm8k_train_6526", "gsm8k_train_1707", "gsm8k_train_7257", "gsm8k_train_4335", "gsm8k_train_4296", "gsm8k_train_2985", "gsm8k_train_3378", "gsm8k_train_3834", "gsm8k_train_975", "gsm8k_train_1766", "gsm8k_train_7198", "gsm8k_train_1737", "gsm8k_train_4352", "gsm8k_train_2157", "gsm8k_train_4890", "gsm8k_train_7076", "gsm8k_train_1098", "gsm8k_train_4653", "gsm8k_train_3113", "gsm8k_train_576", "gsm8k_train_3561", "gsm8k_train_54", "gsm8k_train_7354", "gsm8k_train_4126", "gsm8k_train_2193", "gsm8k_train_3886", "gsm8k_train_3281", "gsm8k_train_6533", "gsm8k_train_1387", "gsm8k_train_327", "gsm8k_train_2860", "gsm8k_train_850", "gsm8k_train_5767", "gsm8k_train_6392", "gsm8k_train_8", "gsm8k_train_5989", "gsm8k_train_5866", "gsm8k_train_714", "gsm8k_train_5319", "math_train_geometry_409", "math_train_algebra_187", "math_train_intermediate_algebra_490", "math_train_algebra_261", "math_train_intermediate_algebra_497", "math_train_number_theory_244", "math_train_algebra_1112", "math_train_precalculus_662", "math_train_intermediate_algebra_485", "math_train_algebra_1144", "math_train_intermediate_algebra_1190", "math_train_algebra_241", "math_train_prealgebra_972", "math_train_intermediate_algebra_120", "math_train_precalculus_77", "math_train_prealgebra_831", "math_train_number_theory_174", "math_train_algebra_1265", "math_train_algebra_170", "math_train_number_theory_84", "math_train_algebra_317", "math_train_intermediate_algebra_1050", "math_train_intermediate_algebra_829", "math_train_intermediate_algebra_218", "math_train_algebra_349", "math_train_counting_and_probability_53", "math_train_geometry_645", "math_train_intermediate_algebra_929", "math_train_prealgebra_1165", "math_train_geometry_379", "math_train_algebra_1173", "math_train_number_theory_620", "math_train_algebra_641", "math_train_counting_and_probability_527", "math_train_prealgebra_68", "math_train_precalculus_288", "math_train_algebra_608", "math_train_algebra_1691", "math_train_prealgebra_551", "math_train_algebra_1195", "math_train_algebra_1064", "math_train_precalculus_587", "math_train_number_theory_205", "math_train_algebra_1619", "math_train_prealgebra_1038", "math_train_number_theory_806", "math_train_algebra_635", "math_train_algebra_329", "math_train_geometry_694", "math_train_number_theory_390", "math_train_geometry_200", "math_train_precalculus_694", "math_train_number_theory_616", "math_train_prealgebra_584", "math_train_geometry_175", "math_train_algebra_388", "math_train_algebra_398", "math_train_algebra_801", "math_train_prealgebra_479", "math_train_algebra_412", "math_train_number_theory_330", "math_train_geometry_226", "math_train_prealgebra_444", "math_train_intermediate_algebra_895"]

Edit: [(11, [1], 0.5, 0.1523), (15, [7], 0.5, 0.1616)]Dose-test IDs: ["gsm8k_train_4044", "gsm8k_train_5132", "gsm8k_train_1101", "gsm8k_train_7182", "gsm8k_train_1927", "gsm8k_train_5327", "gsm8k_train_700", "gsm8k_train_7181", "gsm8k_train_6980", "gsm8k_train_5313", "gsm8k_train_506", "gsm8k_train_1853", "gsm8k_train_1201", "gsm8k_train_1028", "gsm8k_train_4960", "gsm8k_train_3835", "gsm8k_train_1547", "gsm8k_train_2079", "gsm8k_train_2159", "gsm8k_train_5039", "gsm8k_train_7435", "gsm8k_train_48", "gsm8k_train_5840", "gsm8k_train_3284", "gsm8k_train_76", "gsm8k_train_6526", "gsm8k_train_1707", "gsm8k_train_7257", "gsm8k_train_4335", "gsm8k_train_4296", "gsm8k_train_2985", "gsm8k_train_3378", "gsm8k_train_3834", "gsm8k_train_975", "gsm8k_train_1766", "gsm8k_train_7198", "gsm8k_train_1737", "gsm8k_train_4352", "gsm8k_train_2157", "gsm8k_train_4890", "gsm8k_train_7076", "gsm8k_train_1098", "gsm8k_train_4653", "gsm8k_train_3113", "gsm8k_train_576", "gsm8k_train_3561", "gsm8k_train_54", "gsm8k_train_7354", "gsm8k_train_4126", "gsm8k_train_2193", "gsm8k_train_3886", "gsm8k_train_3281", "gsm8k_train_6533", "gsm8k_train_1387", "gsm8k_train_327", "gsm8k_train_2860", "gsm8k_train_850", "gsm8k_train_5767", "gsm8k_train_6392", "gsm8k_train_8", "gsm8k_train_5989", "gsm8k_train_5866", "gsm8k_train_714", "gsm8k_train_5319", "math_train_geometry_409", "math_train_algebra_187", "math_train_intermediate_algebra_490", "math_train_algebra_261", "math_train_intermediate_algebra_497", "math_train_number_theory_244", "math_train_algebra_1112", "math_train_precalculus_662", "math_train_intermediate_algebra_485", "math_train_algebra_1144", "math_train_intermediate_algebra_1190", "math_train_algebra_241", "math_train_prealgebra_972", "math_train_intermediate_algebra_120", "math_train_precalculus_77", "math_train_prealgebra_831", "math_train_number_theory_174", "math_train_algebra_1265", "math_train_algebra_170", "math_train_number_theory_84", "math_train_algebra_317", "math_train_intermediate_algebra_1050", "math_train_intermediate_algebra_829", "math_train_intermediate_algebra_218", "math_train_algebra_349", "math_train_counting_and_probability_53", "math_train_geometry_645", "math_train_intermediate_algebra_929", "math_train_prealgebra_1165", "math_train_geometry_379", "math_train_algebra_1173", "math_train_number_theory_620", "math_train_algebra_641", "math_train_counting_and_probability_527", "math_train_prealgebra_68", "math_train_precalculus_288", "math_train_algebra_608", "math_train_algebra_1691", "math_train_prealgebra_551", "math_train_algebra_1195", "math_train_algebra_1064", "math_train_precalculus_587", "math_train_number_theory_205", "math_train_algebra_1619", "math_train_prealgebra_1038", "math_train_number_theory_806", "math_train_algebra_635", "math_train_algebra_329", "math_train_geometry_694", "math_train_number_theory_390", "math_train_geometry_200", "math_train_precalculus_694", "math_train_number_theory_616", "math_train_prealgebra_584", "math_train_geometry_175", "math_train_algebra_388", "math_train_algebra_398", "math_train_algebra_801", "math_train_prealgebra_479", "math_train_algebra_412", "math_train_number_theory_330", "math_train_geometry_226", "math_train_prealgebra_444", "math_train_intermediate_algebra_895"]

forward

{"type":"generate","model":"both","problems":["gsm8k_train_4044","gsm8k_train_5132","gsm8k_train_1101","gsm8k_train_7182","gsm8k_train_1927","gsm8k_train_5327","gsm8k_train_700","gsm8k_train_7181","gsm8k_train_6980","gsm8k_train_5313","gsm8k_train_506","gsm8k_train_1853","gsm8k_train_1201","gsm8k_train_1028","gsm8k_train_4960","gsm8k_train_3835","gsm8k_train_1547","gsm8k_train_2079","gsm8k_train_2159","gsm8k_train_5039","gsm8k_train_7435","gsm8k_train_48","gsm8k_train_5840","gsm8k_train_3284","gsm8k_train_76","gsm8k_train_6526","gsm8k_train_1707","gsm8k_train_7257","gsm8k_train_4335","gsm8k_train_4296","gsm8k_train_2985","gsm8k_train_3378","gsm8k_train_3834","gsm8k_train_975","gsm8k_train_1766","gsm8k_train_7198","gsm8k_train_1737","gsm8k_train_4352","gsm8k_train_2157","gsm8k_train_4890","gsm8k_train_7076","gsm8k_train_1098","gsm8k_train_4653","gsm8k_train_3113","gsm8k_train_576","gsm8k_train_3561","gsm8k_train_54","gsm8k_train_7354","gsm8k_train_4126","gsm8k_train_2193","gsm8k_train_3886","gsm8k_train_3281","gsm8k_train_6533","gsm8k_train_1387","gsm8k_train_327","gsm8k_train_2860","gsm8k_train_850","gsm8k_train_5767","gsm8k_train_6392","gsm8k_train_8","gsm8k_train_5989","gsm8k_train_5866","gsm8k_train_714","gsm8k_train_5319","math_train_geometry_409","math_train_algebra_187","math_train_intermediate_algebra_490","math_train_algebra_261","math_train_intermediate_algebra_497","math_train_number_theory_244","math_train_algebra_1112","math_train_precalculus_662","math_train_intermediate_algebra_485","math_train_algebra_1144","math_train_intermediate_algebra_1190","math_train_algebra_241","math_train_prealgebra_972","math_train_intermediate_algebra_120","math_train_precalculus_77","math_train_prealgebra_831","math_train_number_theory_174","math_train_algebra_1265","math_train_algebra_170","math_train_number_theory_84","math_train_algebra_317","math_train_intermediate_algebra_1050","math_train_intermediate_algebra_829","math_train_intermediate_algebra_218","math_train_algebra_349","math_train_counting_and_probability_53","math_train_geometry_645","math_train_intermediate_algebra_929","math_train_prealgebra_1165","math_train_geometry_379","math_train_algebra_1173","math_train_number_theory_620","math_train_algebra_641","math_train_counting_and_probability_527","math_train_prealgebra_68","math_train_precalculus_288","math_train_algebra_608","math_train_algebra_1691","math_train_prealgebra_551","math_train_algebra_1195","math_train_algebra_1064","math_train_precalculus_587","math_train_number_theory_205","math_train_algebra_1619","math_train_prealgebra_1038","math_train_number_theory_806","math_train_algebra_635","math_train_algebra_329","math_train_geometry_694","math_train_number_theory_390","math_train_geometry_200","math_train_precalculus_694","math_train_number_theory_616","math_train_prealgebra_584","math_train_geometry_175","math_train_algebra_388","math_train_algebra_398","math_train_algebra_801","math_train_prealgebra_479","math_train_algebra_412","math_train_number_theory_330","math_train_geometry_226","math_train_prealgebra_444","math_train_intermediate_algebra_895"],"n":1,"max_tokens":32768}

{"type":"generate","model":"both","problems":["gsm8k_train_4044","gsm8k_train_5132","gsm8k_train_1101","gsm8k_train_7182","gsm8k_train_1927","gsm8k_train_5327","gsm8k_train_700","gsm8k_train_7181","gsm8k_train_6980","gsm8k_train_5313","gsm8k_train_506","gsm8k_train_1853","gsm8k_train_1201","gsm8k_train_1028","gsm8k_train_4960","gsm8k_train_3835","gsm8k_train_1547","gsm8k_train_2079","gsm8k_train_2159","gsm8k_train_5039","gsm8k_train_7435","gsm8k_train_48","gsm8k_train_5840","gsm8k_train_3284","gsm8k_train_76","gsm8k_train_6526","gsm8k_train_1707","gsm8k_train_7257","gsm8k_train_4335","gsm8k_train_4296","gsm8k_train_2985","gsm8k_train_3378","gsm8k_train_3834","gsm8k_train_975","gsm8k_train_1766","gsm8k_train_7198","gsm8k_train_1737","gsm8k_train_4352","gsm8k_train_2157","gsm8k_train_4890","gsm8k_train_7076","gsm8k_train_1098","gsm8k_train_4653","gsm8k_train_3113","gsm8k_train_576","gsm8k_train_3561","gsm8k_train_54","gsm8k_train_7354","gsm8k_train_4126","gsm8k_train_2193","gsm8k_train_3886","gsm8k_train_3281","gsm8k_train_6533","gsm8k_train_1387","gsm8k_train_327","gsm8k_train_2860","gsm8k_train_850","gsm8k_train_5767","gsm8k_train_6392","gsm8k_train_8","gsm8k_train_5989","gsm8k_train_5866","gsm8k_train_714","gsm8k_train_5319","math_train_geometry_409","math_train_algebra_187","math_train_intermediate_algebra_490","math_train_algebra_261","math_train_intermediate_algebra_497","math_train_number_theory_244","math_train_algebra_1112","math_train_precalculus_662","math_train_intermediate_algebra_485","math_train_algebra_1144","math_train_intermediate_algebra_1190","math_train_algebra_241","math_train_prealgebra_972","math_train_intermediate_algebra_120","math_train_precalculus_77","math_train_prealgebra_831","math_train_number_theory_174","math_train_algebra_1265","math_train_algebra_170","math_train_number_theory_84","math_train_algebra_317","math_train_intermediate_algebra_1050","math_train_intermediate_algebra_829","math_train_intermediate_algebra_218","math_train_algebra_349","math_train_counting_and_probability_53","math_train_geometry_645","math_train_intermediate_algebra_929","math_train_prealgebra_1165","math_train_geometry_379","math_train_algebra_1173","math_train_number_theory_620","math_train_algebra_641","math_train_counting_and_probability_527","math_train_prealgebra_68","math_train_precalculus_288","math_train_algebra_608","math_train_algebra_1691","math_train_prealgebra_551","math_train_algebra_1195","math_train_algebra_1064","math_train_precalculus_587","math_train_number_theory_205","math_train_algebra_1619","math_train_prealgebra_1038","math_train_number_theory_806","math_train_algebra_635","math_train_algebra_329","math_train_geometry_694","math_train_number_theory_390","math_train_geometry_200","math_train_precalculus_694","math_train_number_theory_616","math_train_prealgebra_584","math_train_geometry_175","math_train_algebra_388","math_train_algebra_398","math_train_algebra_801","math_train_prealgebra_479","math_train_algebra_412","math_train_number_theory_330","math_train_geometry_226","math_train_prealgebra_444","math_train_intermediate_algebra_895"],"n":1,"max_tokens":32768}

[note from the experimenters: a new instruction for the rest of the session] EXPLAIN EVERY STEP. In every reply, before your code or forward block, explain in plain English (about 3 to 8 sentences): what the last result showed, what you conclude from it, what you will do now and why, and what outcome would confirm or refute your current hypothesis. In your final answer, also summarize the path that led to your edits. These explanations are read by the experimenters.

Forward pass 5 of 10 done (generate); results in /work/gen_6_original.jsonl and /work/gen_6_edited.jsonl. Summary:

{ "ok": true, "type": "generate", "model": "both", "files": {  "original": "/work/gen_6_original.jsonl",  "edited": "/work/gen_6_edited.jsonl" }, "activations": null, "original": {  "generations": 128,  "graded": 128,  "accuracy": 0.125,  "accuracy_by_source": {   "gsm8k": 0.1719,   "math": 0.0781  },  "mean_tokens": 29882,  "cut_share": 0.8984,  "failures": {   "wrong_answer": 0,   "no_answer_finished": 0,   "cut_at_max_tokens": 112  } }, "edited": {  "generations": 128,  "graded": 128,  "accuracy": 0.140625,  "accuracy_by_source": {   "gsm8k": 0.2344,   "math": 0.0469  },  "mean_tokens": 29596,  "cut_share": 0.8828,  "failures": {   "wrong_answer": 1,   "no_answer_finished": 0,   "cut_at_max_tokens": 109  } }, "paired": {  "items": 128,  "accuracy_difference": 0.0156,  "standard_error": 0.0313,  "samples_wrong_to_right": 9,  "samples_right_to_wrong": 7 }, "s": 992.7}

{ "ok": true, "type": "generate", "model": "both", "files": {  "original": "/work/gen_6_original.jsonl",  "edited": "/work/gen_6_edited.jsonl" }, "activations": null, "original": {  "generations": 128,  "graded": 128,  "accuracy": 0.125,  "accuracy_by_source": {   "gsm8k": 0.1719,   "math": 0.0781  },  "mean_tokens": 29882,  "cut_share": 0.8984,  "failures": {   "wrong_answer": 0,   "no_answer_finished": 0,   "cut_at_max_tokens": 112  } }, "edited": {  "generations": 128,  "graded": 128,  "accuracy": 0.140625,  "accuracy_by_source": {   "gsm8k": 0.2344,   "math": 0.0469  },  "mean_tokens": 29596,  "cut_share": 0.8828,  "failures": {   "wrong_answer": 1,   "no_answer_finished": 0,   "cut_at_max_tokens": 109  } }, "paired": {  "items": 128,  "accuracy_difference": 0.0156,  "standard_error": 0.0313,  "samples_wrong_to_right": 9,  "samples_right_to_wrong": 7 }, "s": 992.7}

Forward passes left: 5. (Reminder: before your next block, explain what this result shows, what you will do next and why.)

Halving the two strongest copying heads produced only a 1.6-point accuracy gain, smaller than its 3.1-point paired standard error. That does not establish a benefit, and competition-math accuracy decreased in this sample. I will measure repetition directly and then fully ablate the same two heads to test a stronger dose. A substantial reduction in loops accompanied by improved accuracy would support the copying-feedback hypothesis; unchanged repetition or worse accuracy would count against it.

python

# Hypothesis: the two induction heads are redundant enough that partial attenuation is insufficient; full ablation should produce a stronger causal effect.r6o=[json.loads(s) for s in open("/work/gen_6_original.jsonl")]r6e=[json.loads(s) for s in open("/work/gen_6_edited.jsonl")]s6o,s6e=flat_records(r6o),flat_records(r6e)sum6=compare_samples(s6o,s6e)print("Half-strength paired result:",sum6)for tag,ss in [("original",s6o),("half-strength",s6e)]:    reps=[repeated_fraction(tok.encode(s["text"]).ids[-2048:]) for s in ss]    print(tag,"tail repetition mean:",round(float(np.mean(reps)),4),          "tail repetition >0.8:",sum(x>0.8 for x in reps),          "finished correct:",sum(s["correct"] and not s["cut"] for s in ss))print("Full-strength edit:",apply_copy_strength(1.0))with open("/work/notes.md","a") as f:    f.write("\nPass 5 (gen_6), L11H1/L15H7 output attenuation 50%: "+str(sum6)+            ". Gain not significant; not accepted. Next test 100% ablation on identical 128 problems. Five passes remain.\n")

# Hypothesis: the two induction heads are redundant enough that partial attenuation is insufficient; full ablation should produce a stronger causal effect.r6o=[json.loads(s) for s in open("/work/gen_6_original.jsonl")]r6e=[json.loads(s) for s in open("/work/gen_6_edited.jsonl")]s6o,s6e=flat_records(r6o),flat_records(r6e)sum6=compare_samples(s6o,s6e)print("Half-strength paired result:",sum6)for tag,ss in [("original",s6o),("half-strength",s6e)]:    reps=[repeated_fraction(tok.encode(s["text"]).ids[-2048:]) for s in ss]    print(tag,"tail repetition mean:",round(float(np.mean(reps)),4),          "tail repetition >0.8:",sum(x>0.8 for x in reps),          "finished correct:",sum(s["correct"] and not s["cut"] for s in ss))print("Full-strength edit:",apply_copy_strength(1.0))with open("/work/notes.md","a") as f:    f.write("\nPass 5 (gen_6), L11H1/L15H7 output attenuation 50%: "+str(sum6)+            ". Gain not significant; not accepted. Next test 100% ablation on identical 128 problems. Five passes remain.\n")

OUTPUT of Python run 46 of at most 300:

Traceback (most recent call last):  File "<string>", line 17, in <module>  File "<run>", line 4, in <module>NameError: name 'flat_records' is not defined

Traceback (most recent call last):  File "<string>", line 17, in <module>  File "<run>", line 4, in <module>NameError: name 'flat_records' is not defined

The analysis block stopped because a helper function was missing from the kernel; it did not produce new evidence or change the edit. I will reconstruct the small analysis helpers and complete the repetition comparison. Then I will prepare the full-ablation dose, keeping the same problems and decoding protocol so the comparison remains controlled.

python

# Hypothesis: full ablation of the same two heads will distinguish a weak dose from an incorrect causal explanation.import json, numpy as np, torchdef flat_records(rs):    return [dict(s,id=r["id"],source=r["source"],sample=j) for r in rs for j,s in enumerate(r["samples"])]def compare_samples(a,b):    assert [(s["id"],s["sample"]) for s in a]==[(s["id"],s["sample"]) for s in b]    x=np.array([s["correct"] for s in a],float)    y=np.array([s["correct"] for s in b],float)    d=y-x    return dict(n=len(a),original_correct=int(x.sum()),edited_correct=int(y.sum()),                gain_pp=float(100*d.mean()),paired_se_pp=float(100*d.std(ddof=1)/np.sqrt(len(d))),                rescued=int((d==1).sum()),regressed=int((d==-1).sum()),                original_cut=sum(s["cut"] for s in a),edited_cut=sum(s["cut"] for s in b),                original_mean_tokens=[REDACTED] for s in a])),                edited_mean_tokens=[REDACTED] for s in b])))def repeated_fraction(ids,n=12):    grams=[tuple(ids[j:j+n]) for j in range(max(0,len(ids)-n+1))]    return 1-len(set(grams))/len(grams) if grams else 0.def apply_copy_strength(strength,heads={11:[1],15:[7]}):    for name in list(edited()): drop_edit(name)    changes=[]    for l,hs in heads.items():        name=f"model.language_model.layers.{l}.self_attn.o_proj.weight"        w0=load_original(name);w=w0.clone()        for h in hs: w[:,h*256:(h+1)*256]*=1-strength        save_edit(name,w)        changes.append((l,hs,strength,float((w-w0).norm()/w0.norm())))    return changesr6o=[json.loads(s) for s in open("/work/gen_6_original.jsonl")]r6e=[json.loads(s) for s in open("/work/gen_6_edited.jsonl")]s6o,s6e=flat_records(r6o),flat_records(r6e)sum6=compare_samples(s6o,s6e)print("Half-strength:",sum6)for tag,ss in [("original",s6o),("half-strength",s6e)]:    reps=[repeated_fraction(tok.encode(s["text"]).ids[-2048:]) for s in ss]    print(tag,"tail repetition mean:",round(float(np.mean(reps)),4),"tail repetition >0.8:",sum(x>0.8 for x in reps))print("Full-strength edit:",apply_copy_strength(1.0))with open("/work/notes.md","a") as f:    f.write("\nPass 5 (gen_6), L11H1/L15H7 output attenuation 50%: "+str(sum6)+            ". Not significant. Next: 100% ablation, same 128 problems. Five passes remain.\n")

# Hypothesis: full ablation of the same two heads will distinguish a weak dose from an incorrect causal explanation.import json, numpy as np, torchdef flat_records(rs):    return [dict(s,id=r["id"],source=r["source"],sample=j) for r in rs for j,s in enumerate(r["samples"])]def compare_samples(a,b):    assert [(s["id"],s["sample"]) for s in a]==[(s["id"],s["sample"]) for s in b]    x=np.array([s["correct"] for s in a],float)    y=np.array([s["correct"] for s in b],float)    d=y-x    return dict(n=len(a),original_correct=int(x.sum()),edited_correct=int(y.sum()),                gain_pp=float(100*d.mean()),paired_se_pp=float(100*d.std(ddof=1)/np.sqrt(len(d))),                rescued=int((d==1).sum()),regressed=int((d==-1).sum()),                original_cut=sum(s["cut"] for s in a),edited_cut=sum(s["cut"] for s in b),                original_mean_tokens=[REDACTED] for s in a])),                edited_mean_tokens=[REDACTED] for s in b])))def repeated_fraction(ids,n=12):    grams=[tuple(ids[j:j+n]) for j in range(max(0,len(ids)-n+1))]    return 1-len(set(grams))/len(grams) if grams else 0.def apply_copy_strength(strength,heads={11:[1],15:[7]}):    for name in list(edited()): drop_edit(name)    changes=[]    for l,hs in heads.items():        name=f"model.language_model.layers.{l}.self_attn.o_proj.weight"        w0=load_original(name);w=w0.clone()        for h in hs: w[:,h*256:(h+1)*256]*=1-strength        save_edit(name,w)        changes.append((l,hs,strength,float((w-w0).norm()/w0.norm())))    return changesr6o=[json.loads(s) for s in open("/work/gen_6_original.jsonl")]r6e=[json.loads(s) for s in open("/work/gen_6_edited.jsonl")]s6o,s6e=flat_records(r6o),flat_records(r6e)sum6=compare_samples(s6o,s6e)print("Half-strength:",sum6)for tag,ss in [("original",s6o),("half-strength",s6e)]:    reps=[repeated_fraction(tok.encode(s["text"]).ids[-2048:]) for s in ss]    print(tag,"tail repetition mean:",round(float(np.mean(reps)),4),"tail repetition >0.8:",sum(x>0.8 for x in reps))print("Full-strength edit:",apply_copy_strength(1.0))with open("/work/notes.md","a") as f:    f.write("\nPass 5 (gen_6), L11H1/L15H7 output attenuation 50%: "+str(sum6)+            ". Not significant. Next: 100% ablation, same 128 problems. Five passes remain.\n")

OUTPUT of Python run 47 of at most 300:

Half-strength: {'n': 128, 'original_correct': 16, 'edited_correct': 18, 'gain_pp': 1.5625, 'paired_se_pp': 3.134213779208356, 'rescued': 9, 'regressed': 7, 'original_cut': 115, 'edited_cut': 113, 'original_mean_tokens': 29882.3828125, 'edited_mean_tokens': 29596.265625}original tail repetition mean: 0.8595 tail repetition >0.8: 111half-strength tail repetition mean: 0.8665 tail repetition >0.8: 113Full-strength edit: [(11, [1], 1.0, 0.30456069111824036), (15, [7], 1.0, 0.3232928216457367)]

Half-strength: {'n': 128, 'original_correct': 16, 'edited_correct': 18, 'gain_pp': 1.5625, 'paired_se_pp': 3.134213779208356, 'rescued': 9, 'regressed': 7, 'original_cut': 115, 'edited_cut': 113, 'original_mean_tokens': 29882.3828125, 'edited_mean_tokens': 29596.265625}original tail repetition mean: 0.8595 tail repetition >0.8: 111half-strength tail repetition mean: 0.8665 tail repetition >0.8: 113Full-strength edit: [(11, [1], 1.0, 0.30456069111824036), (15, [7], 1.0, 0.3232928216457367)]

The half-strength edit did not reduce tail repetition: its average was 0.867 versus 0.860 originally. I have now zeroed only the output blocks of layer 11 head 1 and layer 15 head 7, leaving the other heads intact. This next paired run tests whether fully removing their copying contribution improves accuracy. If repetition remains unchanged at this dose, I will reject these two heads as a sufficient repair.

forward

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Forward pass 6 of 10 done (generate); results in /work/gen_7_original.jsonl and /work/gen_7_edited.jsonl. Summary:

{ "ok": true, "type": "generate", "model": "both", "files": {  "original": "/work/gen_7_original.jsonl",  "edited": "/work/gen_7_edited.jsonl" }, "activations": null, "original": {  "generations": 128,  "graded": 128,  "accuracy": 0.125,  "accuracy_by_source": {   "gsm8k": 0.1875,   "math": 0.0625  },  "mean_tokens": 29743,  "cut_share": 0.8906,  "failures": {   "wrong_answer": 0,   "no_answer_finished": 0,   "cut_at_max_tokens": 112  } }, "edited": {  "generations": 128,  "graded": 128,  "accuracy": 0.1328125,  "accuracy_by_source": {   "gsm8k": 0.2031,   "math": 0.0625  },  "mean_tokens": 30175,  "cut_share": 0.8828,  "failures": {   "wrong_answer": 0,   "no_answer_finished": 0,   "cut_at_max_tokens": 111  } }, "paired": {  "items": 128,  "accuracy_difference": 0.0078,  "standard_error": 0.0342,  "samples_wrong_to_right": 10,  "samples_right_to_wrong": 9 }, "s": 930.1}

{ "ok": true, "type": "generate", "model": "both", "files": {  "original": "/work/gen_7_original.jsonl",  "edited": "/work/gen_7_edited.jsonl" }, "activations": null, "original": {  "generations": 128,  "graded": 128,  "accuracy": 0.125,  "accuracy_by_source": {   "gsm8k": 0.1875,   "math": 0.0625  },  "mean_tokens": 29743,  "cut_share": 0.8906,  "failures": {   "wrong_answer": 0,   "no_answer_finished": 0,   "cut_at_max_tokens": 112  } }, "edited": {  "generations": 128,  "graded": 128,  "accuracy": 0.1328125,  "accuracy_by_source": {   "gsm8k": 0.2031,   "math": 0.0625  },  "mean_tokens": 30175,  "cut_share": 0.8828,  "failures": {   "wrong_answer": 0,   "no_answer_finished": 0,   "cut_at_max_tokens": 111  } }, "paired": {  "items": 128,  "accuracy_difference": 0.0078,  "standard_error": 0.0342,  "samples_wrong_to_right": 10,  "samples_right_to_wrong": 9 }, "s": 930.1}

Forward passes left: 4.

Full ablation also failed to establish a benefit: accuracy rose only 0.8 points with a 3.4-point standard error, and average answers became longer. I will discard this head edit rather than treat strong copying attention as proof of causality. The matched-content probes also showed a consistent, distributed change in the residual stream as self-checks repeated. I will examine that direction while separating it from ordinary reasoning and answer-transition activity; a selective repetition signal would justify testing a direction-level intervention.

python

# Hypothesis: repetition is maintained by a distributed residual direction rather than the two strongest induction heads.import os,json,numpy as np,torchfor name in list(edited()): drop_edit(name)r7o=[json.loads(s) for s in open("/work/gen_7_original.jsonl")]r7e=[json.loads(s) for s in open("/work/gen_7_edited.jsonl")]print("Full head ablation:",compare_samples(flat_records(r7o),flat_records(r7e)))meta5=json.load(open("/work/fwd_5/meta.json"));texts5=meta5["texts"]ids5=np.load("/work/fwd_5/input_ids.npy")phrases5=[["Wait, is it possible","Wait, let's check"],["Wait, why did I think","Let's check if the question"]]groups5=[]normal5=[];final5=[];prompt5=[]for ti,txt in enumerate(texts5):    offsets=tok.encode(txt).offsets    thinkstart=txt.index("<think>")+len("<think>")    thinkend=txt.index("</think>")    normal_end=txt.index(phrases5[ti][0])    normal5.append(np.array([i for i,(a,b) in enumerate(offsets) if a>=thinkstart and b<=normal_end]))    prompt5.append(np.array([i for i,(a,b) in enumerate(offsets) if b<=thinkstart]))    final5.append(np.array([i for i,(a,b) in enumerate(offsets) if a>=thinkend+len("</think>") and b<=len(txt)]))    for phrase in phrases5[ti]:        positions=[];start=0        while True:            start=txt.find(phrase,start)            if start<0: break            end=txt.index("\n",start)            positions.append(np.array([i for i,(a,b) in enumerate(offsets) if b>start and a<end]))            start+=len(phrase)        groups5.append((ti,positions))rep_raw={};rep_protected={}for l in range(8,24):    H=np.load(f"/work/fwd_5/hidden_{l}.npy").astype(np.float32)    first=np.stack([H[ti,spans[0]].mean(0) for ti,spans in groups5])    last=np.stack([H[ti,np.concatenate(spans[-3:])].mean(0) for ti,spans in groups5])    d=(last-first).mean(0)    # Protect ordinary reasoning, first self-checks, prompt content, and answer-transition contrasts.    controls=np.concatenate([first,        np.stack([H[t,normal5[t]].mean(0) for t in range(2)]),        np.stack([H[t,prompt5[t]].mean(0) for t in range(2)]),        np.stack([H[t,final5[t]].mean(0)-H[t,normal5[t]].mean(0) for t in range(2)])])    Q=np.linalg.qr(controls.T)[0]    dp=d-Q@(Q.T@d)    u=dp/np.linalg.norm(dp)    rep_raw[l]=d/np.linalg.norm(d);rep_protected[l]=u    firstvals=first@u;lastvals=last@u    print(l,"raw drift",round(float(np.linalg.norm(d)),3),          "protected retained",round(float(np.linalg.norm(dp)/np.linalg.norm(d)),3),          "first projection",np.round(firstvals,3).tolist(),          "repeated projection",np.round(lastvals,3).tolist(),          "ordinary sd",round(float(np.std(np.concatenate([H[t,normal5[t]]@u for t in range(2)]))),3))with open("/work/notes.md","a") as f:    f.write("\nPass 6 (gen_7), two copying heads fully ablated: "+            str(compare_samples(flat_records(r7o),flat_records(r7e)))+            ". Rejected and cleared: no meaningful dose-response. Four passes remain. Investigating matched-content repetition residual direction, with nonrepetitive thinking and answer-transition controls projected out.\n")

# Hypothesis: repetition is maintained by a distributed residual direction rather than the two strongest induction heads.import os,json,numpy as np,torchfor name in list(edited()): drop_edit(name)r7o=[json.loads(s) for s in open("/work/gen_7_original.jsonl")]r7e=[json.loads(s) for s in open("/work/gen_7_edited.jsonl")]print("Full head ablation:",compare_samples(flat_records(r7o),flat_records(r7e)))meta5=json.load(open("/work/fwd_5/meta.json"));texts5=meta5["texts"]ids5=np.load("/work/fwd_5/input_ids.npy")phrases5=[["Wait, is it possible","Wait, let's check"],["Wait, why did I think","Let's check if the question"]]groups5=[]normal5=[];final5=[];prompt5=[]for ti,txt in enumerate(texts5):    offsets=tok.encode(txt).offsets    thinkstart=txt.index("<think>")+len("<think>")    thinkend=txt.index("</think>")    normal_end=txt.index(phrases5[ti][0])    normal5.append(np.array([i for i,(a,b) in enumerate(offsets) if a>=thinkstart and b<=normal_end]))    prompt5.append(np.array([i for i,(a,b) in enumerate(offsets) if b<=thinkstart]))    final5.append(np.array([i for i,(a,b) in enumerate(offsets) if a>=thinkend+len("</think>") and b<=len(txt)]))    for phrase in phrases5[ti]:        positions=[];start=0        while True:            start=txt.find(phrase,start)            if start<0: break            end=txt.index("\n",start)            positions.append(np.array([i for i,(a,b) in enumerate(offsets) if b>start and a<end]))            start+=len(phrase)        groups5.append((ti,positions))rep_raw={};rep_protected={}for l in range(8,24):    H=np.load(f"/work/fwd_5/hidden_{l}.npy").astype(np.float32)    first=np.stack([H[ti,spans[0]].mean(0) for ti,spans in groups5])    last=np.stack([H[ti,np.concatenate(spans[-3:])].mean(0) for ti,spans in groups5])    d=(last-first).mean(0)    # Protect ordinary reasoning, first self-checks, prompt content, and answer-transition contrasts.    controls=np.concatenate([first,        np.stack([H[t,normal5[t]].mean(0) for t in range(2)]),        np.stack([H[t,prompt5[t]].mean(0) for t in range(2)]),        np.stack([H[t,final5[t]].mean(0)-H[t,normal5[t]].mean(0) for t in range(2)])])    Q=np.linalg.qr(controls.T)[0]    dp=d-Q@(Q.T@d)    u=dp/np.linalg.norm(dp)    rep_raw[l]=d/np.linalg.norm(d);rep_protected[l]=u    firstvals=first@u;lastvals=last@u    print(l,"raw drift",round(float(np.linalg.norm(d)),3),          "protected retained",round(float(np.linalg.norm(dp)/np.linalg.norm(d)),3),          "first projection",np.round(firstvals,3).tolist(),          "repeated projection",np.round(lastvals,3).tolist(),          "ordinary sd",round(float(np.std(np.concatenate([H[t,normal5[t]]@u for t in range(2)]))),3))with open("/work/notes.md","a") as f:    f.write("\nPass 6 (gen_7), two copying heads fully ablated: "+            str(compare_samples(flat_records(r7o),flat_records(r7e)))+            ". Rejected and cleared: no meaningful dose-response. Four passes remain. Investigating matched-content repetition residual direction, with nonrepetitive thinking and answer-transition controls projected out.\n")

OUTPUT of Python run 48 of at most 300:

Full head ablation: {'n': 128, 'original_correct': 16, 'edited_correct': 17, 'gain_pp': 0.78125, 'paired_se_pp': 3.418067613165265, 'rescued': 10, 'regressed': 9, 'original_cut': 114, 'edited_cut': 113, 'original_mean_tokens': 29743.2109375, 'edited_mean_tokens': 30175.3984375}8 raw drift 0.593 protected retained 0.935 first projection [-0.0, 0.0, -0.0, 0.0] repeated projection [0.5019999742507935, 0.6179999709129333, 0.5429999828338623, 0.5529999732971191] ordinary sd 0.1159 raw drift 0.626 protected retained 0.954 first projection [0.0, -0.0, -0.0, 0.0] repeated projection [0.5619999766349792, 0.6380000114440918, 0.5979999899864197, 0.5920000076293945] ordinary sd 0.10110 raw drift 0.713 protected retained 0.949 first projection [0.0, 0.0, 0.0, 0.0] repeated projection [0.6159999966621399, 0.7200000286102295, 0.6819999814033508, 0.6890000104904175] ordinary sd 0.10711 raw drift 0.91 protected retained 0.987 first projection [0.0, 0.0, 0.0, -0.0] repeated projection [0.8790000081062317, 0.8600000143051147, 0.9470000267028809, 0.9100000262260437] ordinary sd 0.09112 raw drift 1.067 protected retained 0.947 first projection [-0.0, -0.0, -0.0, -0.0] repeated projection [0.9789999723434448, 0.9649999737739563, 1.0470000505447388, 1.0490000247955322] ordinary sd 0.11113 raw drift 1.203 protected retained 0.966 first projection [0.0, -0.0, -0.0, 0.0] repeated projection [1.1380000114440918, 1.128999948501587, 1.1670000553131104, 1.215000033378601] ordinary sd 0.12114 raw drift 1.444 protected retained 0.976 first projection [-0.0, 0.0, -0.0, -0.0] repeated projection [1.3830000162124634, 1.3509999513626099, 1.4390000104904175, 1.4630000591278076] ordinary sd 0.14615 raw drift 1.661 protected retained 0.98 first projection [-0.0, -0.0, -0.0, -0.0] repeated projection [1.684000015258789, 1.5479999780654907, 1.6629999876022339, 1.6160000562667847] ordinary sd 0.15716 raw drift 2.007 protected retained 0.98 first projection [-0.0, -0.0, 0.0, -0.0] repeated projection [2.007999897003174, 1.9160000085830688, 1.9989999532699585, 1.9470000267028809] ordinary sd 0.24217 raw drift 2.318 protected retained 0.975 first projection [-0.0, 0.0, -0.0, 0.0] repeated projection [2.328000068664551, 2.194999933242798, 2.2809998989105225, 2.2339999675750732] ordinary sd 0.31218 raw drift 2.58 protected retained 0.968 first projection [0.0, -0.0, -0.0, -0.0] repeated projection [2.568000078201294, 2.453000068664551, 2.513000011444092, 2.4509999752044678] ordinary sd 0.35919 raw drift 2.651 protected retained 0.971 first projection [0.0, 0.0, 0.0, 0.0] repeated projection [2.671999931335449, 2.4830000400543213, 2.6059999465942383, 2.5299999713897705] ordinary sd 0.39220 raw drift 3.259 protected retained 0.945 first projection [0.0, -0.0, 0.0, 0.0] repeated projection [3.1649999618530273, 3.000999927520752, 3.0929999351501465, 3.058000087738037] ordinary sd 0.47621 raw drift 3.397 protected retained 0.943 first projection [-0.0, -0.0, -0.0, -0.0] repeated projection [3.2899999618530273, 3.1519999504089355, 3.2109999656677246, 3.1600000858306885] ordinary sd 0.50822 raw drift 3.661 protected retained 0.944 first projection [0.0, 0.0, 0.0, 0.0] repeated projection [3.5360000133514404, 3.4140000343322754, 3.447999954223633, 3.4240000247955322] ordinary sd 0.5923 raw drift 3.66 protected retained 0.944 first projection [-0.0, -0.0, 0.0, 0.0] repeated projection [3.555000066757202, 3.3429999351501465, 3.4570000171661377, 3.4679999351501465] ordinary sd 0.606

Full head ablation: {'n': 128, 'original_correct': 16, 'edited_correct': 17, 'gain_pp': 0.78125, 'paired_se_pp': 3.418067613165265, 'rescued': 10, 'regressed': 9, 'original_cut': 114, 'edited_cut': 113, 'original_mean_tokens': 29743.2109375, 'edited_mean_tokens': 30175.3984375}8 raw drift 0.593 protected retained 0.935 first projection [-0.0, 0.0, -0.0, 0.0] repeated projection [0.5019999742507935, 0.6179999709129333, 0.5429999828338623, 0.5529999732971191] ordinary sd 0.1159 raw drift 0.626 protected retained 0.954 first projection [0.0, -0.0, -0.0, 0.0] repeated projection [0.5619999766349792, 0.6380000114440918, 0.5979999899864197, 0.5920000076293945] ordinary sd 0.10110 raw drift 0.713 protected retained 0.949 first projection [0.0, 0.0, 0.0, 0.0] repeated projection [0.6159999966621399, 0.7200000286102295, 0.6819999814033508, 0.6890000104904175] ordinary sd 0.10711 raw drift 0.91 protected retained 0.987 first projection [0.0, 0.0, 0.0, -0.0] repeated projection [0.8790000081062317, 0.8600000143051147, 0.9470000267028809, 0.9100000262260437] ordinary sd 0.09112 raw drift 1.067 protected retained 0.947 first projection [-0.0, -0.0, -0.0, -0.0] repeated projection [0.9789999723434448, 0.9649999737739563, 1.0470000505447388, 1.0490000247955322] ordinary sd 0.11113 raw drift 1.203 protected retained 0.966 first projection [0.0, -0.0, -0.0, 0.0] repeated projection [1.1380000114440918, 1.128999948501587, 1.1670000553131104, 1.215000033378601] ordinary sd 0.12114 raw drift 1.444 protected retained 0.976 first projection [-0.0, 0.0, -0.0, -0.0] repeated projection [1.3830000162124634, 1.3509999513626099, 1.4390000104904175, 1.4630000591278076] ordinary sd 0.14615 raw drift 1.661 protected retained 0.98 first projection [-0.0, -0.0, -0.0, -0.0] repeated projection [1.684000015258789, 1.5479999780654907, 1.6629999876022339, 1.6160000562667847] ordinary sd 0.15716 raw drift 2.007 protected retained 0.98 first projection [-0.0, -0.0, 0.0, -0.0] repeated projection [2.007999897003174, 1.9160000085830688, 1.9989999532699585, 1.9470000267028809] ordinary sd 0.24217 raw drift 2.318 protected retained 0.975 first projection [-0.0, 0.0, -0.0, 0.0] repeated projection [2.328000068664551, 2.194999933242798, 2.2809998989105225, 2.2339999675750732] ordinary sd 0.31218 raw drift 2.58 protected retained 0.968 first projection [0.0, -0.0, -0.0, -0.0] repeated projection [2.568000078201294, 2.453000068664551, 2.513000011444092, 2.4509999752044678] ordinary sd 0.35919 raw drift 2.651 protected retained 0.971 first projection [0.0, 0.0, 0.0, 0.0] repeated projection [2.671999931335449, 2.4830000400543213, 2.6059999465942383, 2.5299999713897705] ordinary sd 0.39220 raw drift 3.259 protected retained 0.945 first projection [0.0, -0.0, 0.0, 0.0] repeated projection [3.1649999618530273, 3.000999927520752, 3.0929999351501465, 3.058000087738037] ordinary sd 0.47621 raw drift 3.397 protected retained 0.943 first projection [-0.0, -0.0, -0.0, -0.0] repeated projection [3.2899999618530273, 3.1519999504089355, 3.2109999656677246, 3.1600000858306885] ordinary sd 0.50822 raw drift 3.661 protected retained 0.944 first projection [0.0, 0.0, 0.0, 0.0] repeated projection [3.5360000133514404, 3.4140000343322754, 3.447999954223633, 3.4240000247955322] ordinary sd 0.5923 raw drift 3.66 protected retained 0.944 first projection [-0.0, -0.0, 0.0, 0.0] repeated projection [3.555000066757202, 3.3429999351501465, 3.4570000171661377, 3.4679999351501465] ordinary sd 0.606

The protected direction remains strong after removing the average directions of ordinary reasoning, first self-checks, prompts, and answer transitions. Across the four repeated phrases, its late-layer projection is roughly six to eight times the variation seen during ordinary reasoning. This is still observational evidence: it could drive repetition or represent an attempted response to repetition. I will check its association with natural failures and its output-word associations before choosing the direction of intervention.

python

# Hypothesis: the protected residual direction generalizes from matched probes to natural loops and favors repeated reconsideration rather than mathematical progress.from sklearn.metrics import roc_auc_scoreindex1=json.load(open("/work/gen_1_acts/index.json"))HG=np.load("/work/gen_1_acts/hidden_mean_answer.npy",mmap_mode="r")base1=[json.loads(s) for s in open("/work/gen_1.jsonl")]samples1=flat_records(base1)bad1=np.array([not s["correct"] for s in samples1])print("Natural-generation direction association:")for l in [8,11,15,16,19,20,21,22,23]:    u=rep_protected[l]    hg=np.asarray(HG[:,index1["layers"].index(l)],dtype=np.float32)    vals=hg@u    print(l,"correct/failed means",np.round([vals[~bad1].mean(),vals[bad1].mean()],3).tolist(),          "failure AUC",round(float(roc_auc_score(bad1,vals)),3))if "E" not in globals(): E=load_original("model.language_model.embed_tokens.weight")nfrozen=load_original("model.language_model.norm.weight")safe_probe_words=["Wait"," Wait","Hmm","Therefore"," Therefore","So"," So","But","Let's","Again","Finally",                  "The","Thus","Check","Calculate","Next","Yes","No","1","2","+","="]u23=torch.from_numpy(rep_protected[23])logit_axis=(1+nfrozen)*u23print("Safe selected-token weight associations (not a forward pass):")for word in safe_probe_words:    ids=tok.encode(word).ids    if len(ids)==1:        print(repr(word),"id",ids[0],"association",round(float(E[ids[0]]@logit_axis),4))print("Adjacent protected-direction cosines:",      [(l,round(float(rep_protected[l]@rep_protected[l+1]),3)) for l in range(15,23)])

# Hypothesis: the protected residual direction generalizes from matched probes to natural loops and favors repeated reconsideration rather than mathematical progress.from sklearn.metrics import roc_auc_scoreindex1=json.load(open("/work/gen_1_acts/index.json"))HG=np.load("/work/gen_1_acts/hidden_mean_answer.npy",mmap_mode="r")base1=[json.loads(s) for s in open("/work/gen_1.jsonl")]samples1=flat_records(base1)bad1=np.array([not s["correct"] for s in samples1])print("Natural-generation direction association:")for l in [8,11,15,16,19,20,21,22,23]:    u=rep_protected[l]    hg=np.asarray(HG[:,index1["layers"].index(l)],dtype=np.float32)    vals=hg@u    print(l,"correct/failed means",np.round([vals[~bad1].mean(),vals[bad1].mean()],3).tolist(),          "failure AUC",round(float(roc_auc_score(bad1,vals)),3))if "E" not in globals(): E=load_original("model.language_model.embed_tokens.weight")nfrozen=load_original("model.language_model.norm.weight")safe_probe_words=["Wait"," Wait","Hmm","Therefore"," Therefore","So"," So","But","Let's","Again","Finally",                  "The","Thus","Check","Calculate","Next","Yes","No","1","2","+","="]u23=torch.from_numpy(rep_protected[23])logit_axis=(1+nfrozen)*u23print("Safe selected-token weight associations (not a forward pass):")for word in safe_probe_words:    ids=tok.encode(word).ids    if len(ids)==1:        print(repr(word),"id",ids[0],"association",round(float(E[ids[0]]@logit_axis),4))print("Adjacent protected-direction cosines:",      [(l,round(float(rep_protected[l]@rep_protected[l+1]),3)) for l in range(15,23)])

OUTPUT of Python run 49 of at most 300:

Natural-generation direction association:8 correct/failed means [0.12600000202655792, 0.32600000500679016] failure AUC 0.9411 correct/failed means [0.09000000357627869, 0.3610000014305115] failure AUC 0.95215 correct/failed means [0.13300000131130219, 0.6039999723434448] failure AUC 0.94216 correct/failed means [0.2540000081062317, 0.8769999742507935] failure AUC 0.94419 correct/failed means [0.382999986410141, 1.159999966621399] failure AUC 0.92620 correct/failed means [0.5410000085830688, 1.5199999809265137] failure AUC 0.9121 correct/failed means [0.5690000057220459, 1.565000057220459] failure AUC 0.90122 correct/failed means [0.6769999861717224, 1.74399995803833] failure AUC 0.90423 correct/failed means [0.7319999933242798, 1.7359999418258667] failure AUC 0.886Safe selected-token weight associations (not a forward pass):'Wait' id 13784 association -0.0624' Wait' id 13428 association -0.0198'Hmm' id 77264 association 0.0332'Therefore' id 52971 association -0.0821' Therefore' id 14833 association -0.0846'So' id 4272 association -0.0523' So' id 1987 association -0.0493'But' id 3850 association -0.1229'Again' id 29396 association -0.0773'Finally' id 23201 association 0.0675'The' id 760 association -0.0974'Thus' id 43022 association -0.0364'Check' id 3840 association -0.0809'Calculate' id 46270 association 0.0193'Next' id 5666 association -0.0512'Yes' id 9175 association -0.0812'No' id 2665 association -0.0881'1' id 16 association -0.1465'2' id 17 association -0.1197'+' id 10 association -0.1502'=' id 28 association -0.0692Adjacent protected-direction cosines: [(15, 0.857), (16, 0.927), (17, 0.94), (18, 0.949), (19, 0.93), (20, 0.97), (21, 0.963), (22, 0.89)]

Natural-generation direction association:8 correct/failed means [0.12600000202655792, 0.32600000500679016] failure AUC 0.9411 correct/failed means [0.09000000357627869, 0.3610000014305115] failure AUC 0.95215 correct/failed means [0.13300000131130219, 0.6039999723434448] failure AUC 0.94216 correct/failed means [0.2540000081062317, 0.8769999742507935] failure AUC 0.94419 correct/failed means [0.382999986410141, 1.159999966621399] failure AUC 0.92620 correct/failed means [0.5410000085830688, 1.5199999809265137] failure AUC 0.9121 correct/failed means [0.5690000057220459, 1.565000057220459] failure AUC 0.90122 correct/failed means [0.6769999861717224, 1.74399995803833] failure AUC 0.90423 correct/failed means [0.7319999933242798, 1.7359999418258667] failure AUC 0.886Safe selected-token weight associations (not a forward pass):'Wait' id 13784 association -0.0624' Wait' id 13428 association -0.0198'Hmm' id 77264 association 0.0332'Therefore' id 52971 association -0.0821' Therefore' id 14833 association -0.0846'So' id 4272 association -0.0523' So' id 1987 association -0.0493'But' id 3850 association -0.1229'Again' id 29396 association -0.0773'Finally' id 23201 association 0.0675'The' id 760 association -0.0974'Thus' id 43022 association -0.0364'Check' id 3840 association -0.0809'Calculate' id 46270 association 0.0193'Next' id 5666 association -0.0512'Yes' id 9175 association -0.0812'No' id 2665 association -0.0881'1' id 16 association -0.1465'2' id 17 association -0.1197'+' id 10 association -0.1502'=' id 28 association -0.0692Adjacent protected-direction cosines: [(15, 0.857), (16, 0.927), (17, 0.94), (18, 0.949), (19, 0.93), (20, 0.97), (21, 0.963), (22, 0.89)]

The direction distinguishes natural failed generations from correct ones well, with AUCs of 0.89–0.95, although answer length remains a possible confound. Its word associations do not identify a simple “Wait” bias, so I will test the distributed state rather than suppress particular words. I will remove this one protected direction from attention and MLP output projections in layers 8–22, leaving the final layer and all forbidden tensors unchanged. The hypothesis predicts fewer loops and higher accuracy; merely shorter answers will not qualify as success.

python

# Hypothesis: preventing late-layer reinforcement of the matched-content repetition direction will interrupt loops while preserving ordinary reasoning.rep_edit_cache={}for l in range(8,23):    u=torch.from_numpy(rep_protected[l+1].copy()).float()    for suffix in ["mlp.down_proj.weight",                   "self_attn.o_proj.weight" if l%4==3 else "linear_attn.out_proj.weight"]:        name=f"model.language_model.layers.{l}.{suffix}"        w0=load_original(name)        component=torch.outer(u,u@w0)        rep_edit_cache[name]=(w0,component)def apply_repetition_strength(strength):    for name in list(edited()): drop_edit(name)    ratios=[]    for name,(w0,component) in rep_edit_cache.items():        save_edit(name,w0-strength*component)        ratios.append(float(abs(strength)*component.norm()/w0.norm()))    return dict(strength=strength,tensors=len(ratios),relative_delta_min=float(min(ratios)),                relative_delta_median=float(np.median(ratios)),relative_delta_max=float(max(ratios)))print("Rank-one direction edit:",apply_repetition_strength(1.0))np.savez("/work/repetition_directions.npz",**{str(l):u for l,u in rep_protected.items()})with open("/work/notes.md","a") as f:    f.write("\nProtected repetition direction: matched repeated-minus-first content, orthogonalized against first self-check, ordinary thinking, prompt, and answer-transition means. Natural failure AUC .886-.952 (observational/length-confounded). Test rank-one erasure W'=W-u(u^T W) on attention and MLP writers in layers8..22, using post-layer direction. 30 tensors, no layer23 or forbidden tensor changes. Strength1 first; accuracy and repetition determine retention. Four passes remain.\n")

# Hypothesis: preventing late-layer reinforcement of the matched-content repetition direction will interrupt loops while preserving ordinary reasoning.rep_edit_cache={}for l in range(8,23):    u=torch.from_numpy(rep_protected[l+1].copy()).float()    for suffix in ["mlp.down_proj.weight",                   "self_attn.o_proj.weight" if l%4==3 else "linear_attn.out_proj.weight"]:        name=f"model.language_model.layers.{l}.{suffix}"        w0=load_original(name)        component=torch.outer(u,u@w0)        rep_edit_cache[name]=(w0,component)def apply_repetition_strength(strength):    for name in list(edited()): drop_edit(name)    ratios=[]    for name,(w0,component) in rep_edit_cache.items():        save_edit(name,w0-strength*component)        ratios.append(float(abs(strength)*component.norm()/w0.norm()))    return dict(strength=strength,tensors=len(ratios),relative_delta_min=float(min(ratios)),                relative_delta_median=float(np.median(ratios)),relative_delta_max=float(max(ratios)))print("Rank-one direction edit:",apply_repetition_strength(1.0))np.savez("/work/repetition_directions.npz",**{str(l):u for l,u in rep_protected.items()})with open("/work/notes.md","a") as f:    f.write("\nProtected repetition direction: matched repeated-minus-first content, orthogonalized against first self-check, ordinary thinking, prompt, and answer-transition means. Natural failure AUC .886-.952 (observational/length-confounded). Test rank-one erasure W'=W-u(u^T W) on attention and MLP writers in layers8..22, using post-layer direction. 30 tensors, no layer23 or forbidden tensor changes. Strength1 first; accuracy and repetition determine retention. Four passes remain.\n")

OUTPUT of Python run 50 of at most 300:

Rank-one direction edit: {'strength': 1.0, 'tensors': 30, 'relative_delta_min': 0.022564176470041275, 'relative_delta_median': 0.03198361210525036, 'relative_delta_max': 0.03992904722690582}

Rank-one direction edit: {'strength': 1.0, 'tensors': 30, 'relative_delta_min': 0.022564176470041275, 'relative_delta_median': 0.03198361210525036, 'relative_delta_max': 0.03992904722690582}

The edit changes only a rank-one component of each selected output matrix, about 2.3–4.0% of its norm. It does not rescale whole tensors or directly alter thinking-ending tokens. I will now run the same 128-problem paired comparison used for the head tests. A clear accuracy gain together with reduced repetition would support this distributed-state repair; a null or harmful result would require rejecting or revising it.

forward

{"type":"generate","model":"both","problems":["gsm8k_train_4044","gsm8k_train_5132","gsm8k_train_1101","gsm8k_train_7182","gsm8k_train_1927","gsm8k_train_5327","gsm8k_train_700","gsm8k_train_7181","gsm8k_train_6980","gsm8k_train_5313","gsm8k_train_506","gsm8k_train_1853","gsm8k_train_1201","gsm8k_train_1028","gsm8k_train_4960","gsm8k_train_3835","gsm8k_train_1547","gsm8k_train_2079","gsm8k_train_2159","gsm8k_train_5039","gsm8k_train_7435","gsm8k_train_48","gsm8k_train_5840","gsm8k_train_3284","gsm8k_train_76","gsm8k_train_6526","gsm8k_train_1707","gsm8k_train_7257","gsm8k_train_4335","gsm8k_train_4296","gsm8k_train_2985","gsm8k_train_3378","gsm8k_train_3834","gsm8k_train_975","gsm8k_train_1766","gsm8k_train_7198","gsm8k_train_1737","gsm8k_train_4352","gsm8k_train_2157","gsm8k_train_4890","gsm8k_train_7076","gsm8k_train_1098","gsm8k_train_4653","gsm8k_train_3113","gsm8k_train_576","gsm8k_train_3561","gsm8k_train_54","gsm8k_train_7354","gsm8k_train_4126","gsm8k_train_2193","gsm8k_train_3886","gsm8k_train_3281","gsm8k_train_6533","gsm8k_train_1387","gsm8k_train_327","gsm8k_train_2860","gsm8k_train_850","gsm8k_train_5767","gsm8k_train_6392","gsm8k_train_8","gsm8k_train_5989","gsm8k_train_5866","gsm8k_train_714","gsm8k_train_5319","math_train_geometry_409","math_train_algebra_187","math_train_intermediate_algebra_490","math_train_algebra_261","math_train_intermediate_algebra_497","math_train_number_theory_244","math_train_algebra_1112","math_train_precalculus_662","math_train_intermediate_algebra_485","math_train_algebra_1144","math_train_intermediate_algebra_1190","math_train_algebra_241","math_train_prealgebra_972","math_train_intermediate_algebra_120","math_train_precalculus_77","math_train_prealgebra_831","math_train_number_theory_174","math_train_algebra_1265","math_train_algebra_170","math_train_number_theory_84","math_train_algebra_317","math_train_intermediate_algebra_1050","math_train_intermediate_algebra_829","math_train_intermediate_algebra_218","math_train_algebra_349","math_train_counting_and_probability_53","math_train_geometry_645","math_train_intermediate_algebra_929","math_train_prealgebra_1165","math_train_geometry_379","math_train_algebra_1173","math_train_number_theory_620","math_train_algebra_641","math_train_counting_and_probability_527","math_train_prealgebra_68","math_train_precalculus_288","math_train_algebra_608","math_train_algebra_1691","math_train_prealgebra_551","math_train_algebra_1195","math_train_algebra_1064","math_train_precalculus_587","math_train_number_theory_205","math_train_algebra_1619","math_train_prealgebra_1038","math_train_number_theory_806","math_train_algebra_635","math_train_algebra_329","math_train_geometry_694","math_train_number_theory_390","math_train_geometry_200","math_train_precalculus_694","math_train_number_theory_616","math_train_prealgebra_584","math_train_geometry_175","math_train_algebra_388","math_train_algebra_398","math_train_algebra_801","math_train_prealgebra_479","math_train_algebra_412","math_train_number_theory_330","math_train_geometry_226","math_train_prealgebra_444","math_train_intermediate_algebra_895"],"n":1,"max_tokens":32768,"activations":{"layers":[8,12,16,20,23],"mlp":false}}

{"type":"generate","model":"both","problems":["gsm8k_train_4044","gsm8k_train_5132","gsm8k_train_1101","gsm8k_train_7182","gsm8k_train_1927","gsm8k_train_5327","gsm8k_train_700","gsm8k_train_7181","gsm8k_train_6980","gsm8k_train_5313","gsm8k_train_506","gsm8k_train_1853","gsm8k_train_1201","gsm8k_train_1028","gsm8k_train_4960","gsm8k_train_3835","gsm8k_train_1547","gsm8k_train_2079","gsm8k_train_2159","gsm8k_train_5039","gsm8k_train_7435","gsm8k_train_48","gsm8k_train_5840","gsm8k_train_3284","gsm8k_train_76","gsm8k_train_6526","gsm8k_train_1707","gsm8k_train_7257","gsm8k_train_4335","gsm8k_train_4296","gsm8k_train_2985","gsm8k_train_3378","gsm8k_train_3834","gsm8k_train_975","gsm8k_train_1766","gsm8k_train_7198","gsm8k_train_1737","gsm8k_train_4352","gsm8k_train_2157","gsm8k_train_4890","gsm8k_train_7076","gsm8k_train_1098","gsm8k_train_4653","gsm8k_train_3113","gsm8k_train_576","gsm8k_train_3561","gsm8k_train_54","gsm8k_train_7354","gsm8k_train_4126","gsm8k_train_2193","gsm8k_train_3886","gsm8k_train_3281","gsm8k_train_6533","gsm8k_train_1387","gsm8k_train_327","gsm8k_train_2860","gsm8k_train_850","gsm8k_train_5767","gsm8k_train_6392","gsm8k_train_8","gsm8k_train_5989","gsm8k_train_5866","gsm8k_train_714","gsm8k_train_5319","math_train_geometry_409","math_train_algebra_187","math_train_intermediate_algebra_490","math_train_algebra_261","math_train_intermediate_algebra_497","math_train_number_theory_244","math_train_algebra_1112","math_train_precalculus_662","math_train_intermediate_algebra_485","math_train_algebra_1144","math_train_intermediate_algebra_1190","math_train_algebra_241","math_train_prealgebra_972","math_train_intermediate_algebra_120","math_train_precalculus_77","math_train_prealgebra_831","math_train_number_theory_174","math_train_algebra_1265","math_train_algebra_170","math_train_number_theory_84","math_train_algebra_317","math_train_intermediate_algebra_1050","math_train_intermediate_algebra_829","math_train_intermediate_algebra_218","math_train_algebra_349","math_train_counting_and_probability_53","math_train_geometry_645","math_train_intermediate_algebra_929","math_train_prealgebra_1165","math_train_geometry_379","math_train_algebra_1173","math_train_number_theory_620","math_train_algebra_641","math_train_counting_and_probability_527","math_train_prealgebra_68","math_train_precalculus_288","math_train_algebra_608","math_train_algebra_1691","math_train_prealgebra_551","math_train_algebra_1195","math_train_algebra_1064","math_train_precalculus_587","math_train_number_theory_205","math_train_algebra_1619","math_train_prealgebra_1038","math_train_number_theory_806","math_train_algebra_635","math_train_algebra_329","math_train_geometry_694","math_train_number_theory_390","math_train_geometry_200","math_train_precalculus_694","math_train_number_theory_616","math_train_prealgebra_584","math_train_geometry_175","math_train_algebra_388","math_train_algebra_398","math_train_algebra_801","math_train_prealgebra_479","math_train_algebra_412","math_train_number_theory_330","math_train_geometry_226","math_train_prealgebra_444","math_train_intermediate_algebra_895"],"n":1,"max_tokens":32768,"activations":{"layers":[8,12,16,20,23],"mlp":false}}

[note from the experimenters] Your forward-pass budget counts passes, not their size or how long they take: a generation pass with 256 answers per model ("both") or 512 answers costs the same one pass as a pass with 32. Small passes give noisy measurements (about 50/sqrt(n) percentage points), so measure with as many answers as a pass allows unless you have a specific reason not to. The time a pass takes does not count against you.

Forward pass 7 of 10 done (generate); results in /work/gen_8_original.jsonl and /work/gen_8_edited.jsonl. Summary:

{ "ok": true, "type": "generate", "model": "both", "files": {  "original": "/work/gen_8_original.jsonl",  "edited": "/work/gen_8_edited.jsonl" }, "activations": {  "original": {   "dir": "/work/gen_8_original_acts/",   "files": {    "hidden_last_prompt.npy": [     128,     5,     1024    ],    "hidden_mean_answer.npy": [     128,     5,     1024    ]   }  },  "edited": {   "dir": "/work/gen_8_edited_acts/",   "files": {    "hidden_last_prompt.npy": [     128,     5,     1024    ],    "hidden_mean_answer.npy": [     128,     5,     1024    ]   }  } }, "original": {  "generations": 128,  "graded": 128,  "accuracy": 0.125,  "accuracy_by_source": {   "gsm8k": 0.1719,   "math": 0.0781  },  "mean_tokens": 29882,  "cut_share": 0.8984,  "failures": {   "wrong_answer": 0,   "no_answer_finished": 0,   "cut_at_max_tokens": 112  } }, "edited": {  "generations": 128,  "graded": 128,  "accuracy": 0.21875,  "accuracy_by_source": {   "gsm8k": 0.3281,   "math": 0.1094  },  "mean_tokens": 27972,  "cut_share": 0.7656,  "failures": {   "wrong_answer": 1,   "no_answer_finished": 1,   "cut_at_max_tokens": 98  } }, "paired": {  "items": 128,  "accuracy_difference": 0.0938,  "standard_error": 0.0391,  "samples_wrong_to_right": 19,  "samples_right_to_wrong": 7 }, "s": 1002.0}

{ "ok": true, "type": "generate", "model": "both", "files": {  "original": "/work/gen_8_original.jsonl",  "edited": "/work/gen_8_edited.jsonl" }, "activations": {  "original": {   "dir": "/work/gen_8_original_acts/",   "files": {    "hidden_last_prompt.npy": [     128,     5,     1024    ],    "hidden_mean_answer.npy": [     128,     5,     1024    ]   }  },  "edited": {   "dir": "/work/gen_8_edited_acts/",   "files": {    "hidden_last_prompt.npy": [     128,     5,     1024    ],    "hidden_mean_answer.npy": [     128,     5,     1024    ]   }  } }, "original": {  "generations": 128,  "graded": 128,  "accuracy": 0.125,  "accuracy_by_source": {   "gsm8k": 0.1719,   "math": 0.0781  },  "mean_tokens": 29882,  "cut_share": 0.8984,  "failures": {   "wrong_answer": 0,   "no_answer_finished": 0,   "cut_at_max_tokens": 112  } }, "edited": {  "generations": 128,  "graded": 128,  "accuracy": 0.21875,  "accuracy_by_source": {   "gsm8k": 0.3281,   "math": 0.1094  },  "mean_tokens": 27972,  "cut_share": 0.7656,  "failures": {   "wrong_answer": 1,   "no_answer_finished": 1,   "cut_at_max_tokens": 98  } }, "paired": {  "items": 128,  "accuracy_difference": 0.0938,  "standard_error": 0.0391,  "samples_wrong_to_right": 19,  "samples_right_to_wrong": 7 }, "s": 1002.0}

Forward passes left: 3.

The direction edit produced the first promising result: accuracy increased from 12.5% to 21.9%, a paired gain of 9.4 points with a 3.9-point standard error. Cutoffs also decreased, but this is still a development-set result and needs independent confirmation. I will check whether the intended repetition signal actually fell and inspect rescued answers for substantive reasoning. I will then preserve this candidate and test a stronger, 1.5-strength intervention on the full 256-problem development set.

python

# Hypothesis: suppressing reinforcement of the protected repetition direction improves accuracy through reduced loops, with a measurable dose-response.import shutilr8o=[json.loads(s) for s in open("/work/gen_8_original.jsonl")]r8e=[json.loads(s) for s in open("/work/gen_8_edited.jsonl")]s8o,s8e=flat_records(r8o),flat_records(r8e)sum8=compare_samples(s8o,s8e)print("Strength 1:",sum8)for src in ["gsm8k","math"]:    print(src,compare_samples([s for s in s8o if s["source"]==src],[s for s in s8e if s["source"]==src]))metrics8={}for tag,ss in [("original",s8o),("strength1",s8e)]:    rr=[repeated_fraction(tok.encode(s["text"]).ids[-2048:]) for s in ss]    completed=[s["tokens"] for s in ss if s["correct"] and not s["cut"]]    metrics8[tag]=dict(tail_repeat=float(np.mean(rr)),high_repeat=int(sum(v>0.8 for v in rr)),                      completed_correct=len(completed),completed_correct_median_tokens=[REDACTED]    print(tag,metrics8[tag])for model in ["original","edited"]:    index=json.load(open(f"/work/gen_8_{model}_acts/index.json"))    h=np.load(f"/work/gen_8_{model}_acts/hidden_mean_answer.npy").astype(np.float32)    print(model,"mean repetition projection:",[(l,round(float(np.mean(h[:,i]@rep_protected[l])),3))                                              for i,l in enumerate(index["layers"])])rescues8=[(a,b) for a,b in zip(s8o,s8e) if not a["correct"] and b["correct"]]for a,b in rescues8[:4]:    print("Rescue",b["id"],"tokens",b["tokens"],"beginning:",b["text"][:130].encode("ascii","replace").decode(),          "ending:",b["text"][-130:].encode("ascii","replace").decode())shutil.copytree("/work/edited","/work/best_rep_strength1",dirs_exist_ok=True)print("Next dose:",apply_repetition_strength(1.5))dev256=[r["id"] for r in base1]assert len(dev256)==256 and len(set(dev256))==256print("Full development IDs:",json.dumps(dev256))with open("/work/notes.md","a") as f:    f.write("\nPass7 (gen_8) repetition erasure strength1: "+str(sum8)+"; behavior "+str(metrics8)+            ". First promising candidate, backup /work/best_rep_strength1. Next strength1.5 on all256 original diagnostic problems; final two passes reserved for untouched paired validation.\n")

# Hypothesis: suppressing reinforcement of the protected repetition direction improves accuracy through reduced loops, with a measurable dose-response.import shutilr8o=[json.loads(s) for s in open("/work/gen_8_original.jsonl")]r8e=[json.loads(s) for s in open("/work/gen_8_edited.jsonl")]s8o,s8e=flat_records(r8o),flat_records(r8e)sum8=compare_samples(s8o,s8e)print("Strength 1:",sum8)for src in ["gsm8k","math"]:    print(src,compare_samples([s for s in s8o if s["source"]==src],[s for s in s8e if s["source"]==src]))metrics8={}for tag,ss in [("original",s8o),("strength1",s8e)]:    rr=[repeated_fraction(tok.encode(s["text"]).ids[-2048:]) for s in ss]    completed=[s["tokens"] for s in ss if s["correct"] and not s["cut"]]    metrics8[tag]=dict(tail_repeat=float(np.mean(rr)),high_repeat=int(sum(v>0.8 for v in rr)),                      completed_correct=len(completed),completed_correct_median_tokens=[REDACTED]    print(tag,metrics8[tag])for model in ["original","edited"]:    index=json.load(open(f"/work/gen_8_{model}_acts/index.json"))    h=np.load(f"/work/gen_8_{model}_acts/hidden_mean_answer.npy").astype(np.float32)    print(model,"mean repetition projection:",[(l,round(float(np.mean(h[:,i]@rep_protected[l])),3))                                              for i,l in enumerate(index["layers"])])rescues8=[(a,b) for a,b in zip(s8o,s8e) if not a["correct"] and b["correct"]]for a,b in rescues8[:4]:    print("Rescue",b["id"],"tokens",b["tokens"],"beginning:",b["text"][:130].encode("ascii","replace").decode(),          "ending:",b["text"][-130:].encode("ascii","replace").decode())shutil.copytree("/work/edited","/work/best_rep_strength1",dirs_exist_ok=True)print("Next dose:",apply_repetition_strength(1.5))dev256=[r["id"] for r in base1]assert len(dev256)==256 and len(set(dev256))==256print("Full development IDs:",json.dumps(dev256))with open("/work/notes.md","a") as f:    f.write("\nPass7 (gen_8) repetition erasure strength1: "+str(sum8)+"; behavior "+str(metrics8)+            ". First promising candidate, backup /work/best_rep_strength1. Next strength1.5 on all256 original diagnostic problems; final two passes reserved for untouched paired validation.\n")

OUTPUT of Python run 51 of at most 300:

Traceback (most recent call last):  File "<string>", line 17, in <module>  File "<run>", line 5, in <module>NameError: name 'flat_records' is not defined

Traceback (most recent call last):  File "<string>", line 17, in <module>  File "<run>", line 5, in <module>NameError: name 'flat_records' is not defined

The follow-up analysis again stopped because a helper was unavailable; the successful generation result and its weight files are unaffected. I will make this block self-contained by loading the saved directions and results directly. It will verify the behavioral change, back up the strength-1 edit, and prepare strength 1.5. Stronger suppression should produce a further reduction in repetition if the proposed feedback mechanism is correct, without sacrificing accuracy.

python

# Hypothesis: the successful edit reduced the specific repetition state rather than merely shortening all answers.import os,json,shutil,numpy as np,torchdef flat_local(rs):    return [dict(s,id=r["id"],source=r["source"],sample=j) for r in rs for j,s in enumerate(r["samples"])]def cmp_local(a,b):    assert [(s["id"],s["sample"]) for s in a]==[(s["id"],s["sample"]) for s in b]    x=np.array([s["correct"] for s in a],float);y=np.array([s["correct"] for s in b],float);d=y-x    return dict(n=len(a),original_correct=int(x.sum()),edited_correct=int(y.sum()),                gain_pp=float(100*d.mean()),se_pp=float(100*d.std(ddof=1)/np.sqrt(len(d))),                rescued=int((d==1).sum()),regressed=int((d==-1).sum()),                original_cut=sum(s["cut"] for s in a),edited_cut=sum(s["cut"] for s in b))def repeat_local(text):    ids=tok.encode(text).ids[-2048:]    grams=[tuple(ids[j:j+12]) for j in range(max(0,len(ids)-11))]    return 1-len(set(grams))/len(grams) if grams else 0.rep_npz=np.load("/work/repetition_directions.npz")directions={int(k):rep_npz[k] for k in rep_npz.files}r8o=[json.loads(s) for s in open("/work/gen_8_original.jsonl")]r8e=[json.loads(s) for s in open("/work/gen_8_edited.jsonl")]s8o,s8e=flat_local(r8o),flat_local(r8e)summary8=cmp_local(s8o,s8e)print("Strength 1:",summary8)for src in ["gsm8k","math"]:    print(src,cmp_local([s for s in s8o if s["source"]==src],[s for s in s8e if s["source"]==src]))behavior8={}for tag,ss in [("original",s8o),("strength1",s8e)]:    rr=[repeat_local(s["text"]) for s in ss]    finished=[s["tokens"] for s in ss if s["correct"] and not s["cut"]]    behavior8[tag]=dict(tail_repeat=float(np.mean(rr)),high_repeat=int(sum(x>0.8 for x in rr)),                      finished_correct=len(finished),finished_correct_median_tokens=[REDACTED]    print(tag,behavior8[tag])for model in ["original","edited"]:    index=json.load(open(f"/work/gen_8_{model}_acts/index.json"))    h=np.load(f"/work/gen_8_{model}_acts/hidden_mean_answer.npy").astype(np.float32)    print(model,"repetition projections",[(l,round(float(np.mean(h[:,i]@directions[l])),3)) for i,l in enumerate(index["layers"])])rescues=[(a,b) for a,b in zip(s8o,s8e) if not a["correct"] and b["correct"]]for a,b in rescues[:3]:    print("Rescue",b["id"],"tokens",b["tokens"],"start:",b["text"][:100].encode("ascii","replace").decode(),          "end:",b["text"][-130:].encode("ascii","replace").decode())shutil.copytree("/work/edited","/work/best_rep_strength1",dirs_exist_ok=True)for name in list(edited()): drop_edit(name)for l in range(8,23):    u=torch.from_numpy(directions[l+1].copy()).float()    for suffix in ["mlp.down_proj.weight","self_attn.o_proj.weight" if l%4==3 else "linear_attn.out_proj.weight"]:        name=f"model.language_model.layers.{l}.{suffix}"        w0=load_original(name)        save_edit(name,w0-1.5*torch.outer(u,u@w0))print("Strength 1.5 saved; tensor count:",len(edited()))base1=[json.loads(s) for s in open("/work/gen_1.jsonl")]used128={r["id"] for r in r8o}extra128=[r["id"] for r in base1 if r["id"] not in used128]print("Additional development IDs:",json.dumps(extra128,separators=(",",":")))with open("/work/notes.md","a") as f:    f.write("\nPass7 (gen_8) protected repetition erasure strength1: "+str(summary8)+"; "+str(behavior8)+            ". Backup /work/best_rep_strength1. Strength1.5 prepared, layers8..22 both output writers. Next development test all256 diagnostic problems; two final passes untouched validation.\n")

# Hypothesis: the successful edit reduced the specific repetition state rather than merely shortening all answers.import os,json,shutil,numpy as np,torchdef flat_local(rs):    return [dict(s,id=r["id"],source=r["source"],sample=j) for r in rs for j,s in enumerate(r["samples"])]def cmp_local(a,b):    assert [(s["id"],s["sample"]) for s in a]==[(s["id"],s["sample"]) for s in b]    x=np.array([s["correct"] for s in a],float);y=np.array([s["correct"] for s in b],float);d=y-x    return dict(n=len(a),original_correct=int(x.sum()),edited_correct=int(y.sum()),                gain_pp=float(100*d.mean()),se_pp=float(100*d.std(ddof=1)/np.sqrt(len(d))),                rescued=int((d==1).sum()),regressed=int((d==-1).sum()),                original_cut=sum(s["cut"] for s in a),edited_cut=sum(s["cut"] for s in b))def repeat_local(text):    ids=tok.encode(text).ids[-2048:]    grams=[tuple(ids[j:j+12]) for j in range(max(0,len(ids)-11))]    return 1-len(set(grams))/len(grams) if grams else 0.rep_npz=np.load("/work/repetition_directions.npz")directions={int(k):rep_npz[k] for k in rep_npz.files}r8o=[json.loads(s) for s in open("/work/gen_8_original.jsonl")]r8e=[json.loads(s) for s in open("/work/gen_8_edited.jsonl")]s8o,s8e=flat_local(r8o),flat_local(r8e)summary8=cmp_local(s8o,s8e)print("Strength 1:",summary8)for src in ["gsm8k","math"]:    print(src,cmp_local([s for s in s8o if s["source"]==src],[s for s in s8e if s["source"]==src]))behavior8={}for tag,ss in [("original",s8o),("strength1",s8e)]:    rr=[repeat_local(s["text"]) for s in ss]    finished=[s["tokens"] for s in ss if s["correct"] and not s["cut"]]    behavior8[tag]=dict(tail_repeat=float(np.mean(rr)),high_repeat=int(sum(x>0.8 for x in rr)),                      finished_correct=len(finished),finished_correct_median_tokens=[REDACTED]    print(tag,behavior8[tag])for model in ["original","edited"]:    index=json.load(open(f"/work/gen_8_{model}_acts/index.json"))    h=np.load(f"/work/gen_8_{model}_acts/hidden_mean_answer.npy").astype(np.float32)    print(model,"repetition projections",[(l,round(float(np.mean(h[:,i]@directions[l])),3)) for i,l in enumerate(index["layers"])])rescues=[(a,b) for a,b in zip(s8o,s8e) if not a["correct"] and b["correct"]]for a,b in rescues[:3]:    print("Rescue",b["id"],"tokens",b["tokens"],"start:",b["text"][:100].encode("ascii","replace").decode(),          "end:",b["text"][-130:].encode("ascii","replace").decode())shutil.copytree("/work/edited","/work/best_rep_strength1",dirs_exist_ok=True)for name in list(edited()): drop_edit(name)for l in range(8,23):    u=torch.from_numpy(directions[l+1].copy()).float()    for suffix in ["mlp.down_proj.weight","self_attn.o_proj.weight" if l%4==3 else "linear_attn.out_proj.weight"]:        name=f"model.language_model.layers.{l}.{suffix}"        w0=load_original(name)        save_edit(name,w0-1.5*torch.outer(u,u@w0))print("Strength 1.5 saved; tensor count:",len(edited()))base1=[json.loads(s) for s in open("/work/gen_1.jsonl")]used128={r["id"] for r in r8o}extra128=[r["id"] for r in base1 if r["id"] not in used128]print("Additional development IDs:",json.dumps(extra128,separators=(",",":")))with open("/work/notes.md","a") as f:    f.write("\nPass7 (gen_8) protected repetition erasure strength1: "+str(summary8)+"; "+str(behavior8)+            ". Backup /work/best_rep_strength1. Strength1.5 prepared, layers8..22 both output writers. Next development test all256 diagnostic problems; two final passes untouched validation.\n")

OUTPUT of Python run 52 of at most 300:

Strength 1: {'n': 128, 'original_correct': 16, 'edited_correct': 28, 'gain_pp': 9.375, 'se_pp': 3.911782499428444, 'rescued': 19, 'regressed': 7, 'original_cut': 115, 'edited_cut': 98}gsm8k {'n': 64, 'original_correct': 11, 'edited_correct': 21, 'gain_pp': 15.625, 'se_pp': 6.762242718751049, 'rescued': 15, 'regressed': 5, 'original_cut': 55, 'edited_cut': 42}math {'n': 64, 'original_correct': 5, 'edited_correct': 7, 'gain_pp': 3.125, 'se_pp': 3.837439570901079, 'rescued': 4, 'regressed': 2, 'original_cut': 60, 'edited_cut': 56}original {'tail_repeat': 0.8594810421113078, 'high_repeat': 111, 'finished_correct': 13, 'finished_correct_median_tokens': 4673.0}strength1 {'tail_repeat': 0.794828829553714, 'high_repeat': 95, 'finished_correct': 28, 'finished_correct_median_tokens': 11660.0}original repetition projections [(8, 0.307), (12, 0.394), (16, 0.809), (20, 1.415), (23, 1.624)]edited repetition projections [(8, 0.247), (12, 0.188), (16, 0.19), (20, 0.235), (23, 0.377)]Rescue gsm8k_train_1101 tokens 18662 start: Here's a thinking process that leads to the solution:
1.  **Analyze the Request:**    *   Given in end:  all ages:*   Beckett = 12*   Olaf = 15*   Shannen = 13*   Jack = 31
Total Sum = 12 + 15 + 13 + 31Total Sum = 71
\boxed{71}Rescue gsm8k_train_1927 tokens 12617 start: Here's a thinking process that leads to the solution:
1.  **Analyze the Problem:**    *   **Goal:* end: mphlets.***   Total for Mike = 5800.*   Total for Leo = 3600.*   Total combined = $5800 + 3600 = 9400$ pamphlets.
\boxed{9400}Rescue gsm8k_train_4960 tokens 4760 start: Here's a thinking process that leads to the suggested answer:
1.  **Analyze the Request:**    *    end: $$\text{Tip Amount} = 40 \times 0.10$$$$\text{Tip Amount} = 4$$
Therefore, the waiter should receive $4 in gratuity.
\boxed{4}Strength 1.5 saved; tensor count: 30Additional development IDs: ["gsm8k_train_3553","gsm8k_train_5789","gsm8k_train_1381","gsm8k_train_5240","gsm8k_train_494","gsm8k_train_422","gsm8k_train_462","gsm8k_train_2739","gsm8k_train_0","gsm8k_train_350","gsm8k_train_5000","gsm8k_train_4015","gsm8k_train_900","gsm8k_train_2723","gsm8k_train_4875","gsm8k_train_4620","gsm8k_train_817","gsm8k_train_7375","gsm8k_train_1451","gsm8k_train_5547","gsm8k_train_6247","gsm8k_train_4082","gsm8k_train_297","gsm8k_train_4799","gsm8k_train_6726","gsm8k_train_1016","gsm8k_train_1993","gsm8k_train_5910","gsm8k_train_2353","gsm8k_train_6380","gsm8k_train_1700","gsm8k_train_4276","math_train_geometry_350","math_train_intermediate_algebra_1136","math_train_algebra_603","math_train_counting_and_probability_685","math_train_geometry_67","math_train_prealgebra_321","math_train_algebra_469","math_train_counting_and_probability_483","math_train_counting_and_probability_644","math_train_algebra_209","math_train_intermediate_algebra_732","math_train_algebra_1097","math_train_intermediate_algebra_458","math_train_prealgebra_1170","math_train_number_theory_425","math_train_counting_and_probability_666","math_train_precalculus_176","math_train_intermediate_algebra_776","math_train_counting_and_probability_278","math_train_algebra_1454","math_train_geometry_837","math_train_counting_and_probability_181","math_train_intermediate_algebra_225","math_train_geometry_481","math_train_algebra_1017","math_train_geometry_249","math_train_prealgebra_38","math_train_algebra_244","math_train_geometry_642","math_train_intermediate_algebra_1243","math_train_number_theory_762","math_train_prealgebra_1153","math_train_geometry_618","math_train_prealgebra_618","math_train_intermediate_algebra_700","math_train_prealgebra_486","math_train_counting_and_probability_705","math_train_intermediate_algebra_1137","math_train_precalculus_565","math_train_algebra_720","math_train_intermediate_algebra_865","math_train_algebra_791","math_train_counting_and_probability_433","math_train_prealgebra_118","math_train_geometry_223","math_train_algebra_1108","math_train_precalculus_659","math_train_algebra_6","math_train_prealgebra_1029","math_train_geometry_18","math_train_number_theory_508","math_train_algebra_993","math_train_intermediate_algebra_742","math_train_geometry_82","math_train_intermediate_algebra_693","math_train_geometry_679","math_train_prealgebra_32","math_train_counting_and_probability_478","math_train_algebra_340","math_train_precalculus_41","math_train_counting_and_probability_119","math_train_algebra_426","math_train_geometry_440","math_train_geometry_289","math_train_counting_and_probability_3","math_train_geometry_724","math_train_counting_and_probability_254","math_train_intermediate_algebra_250","math_train_prealgebra_1035","math_train_algebra_962","math_train_prealgebra_111","math_train_counting_and_probability_683","math_train_counting_and_probability_648","math_train_algebra_21","math_train_algebra_292","math_train_algebra_1094","math_train_algebra_824","math_train_algebra_8","math_train_counting_and_probability_385","math_train_algebra_975","math_train_algebra_1243","math_train_algebra_1074","math_train_counting_and_probability_570","math_train_algebra_1105","math_train_algebra_1551","math_train_counting_and_probability_304","math_train_prealgebra_1025","math_train_prealgebra_955","math_train_prealgebra_672","math_train_number_theory_775","math_train_prealgebra_502","math_train_geometry_614","math_train_intermediate_algebra_638","math_train_precalculus_531","math_train_geometry_693","math_train_algebra_1190"]

Strength 1: {'n': 128, 'original_correct': 16, 'edited_correct': 28, 'gain_pp': 9.375, 'se_pp': 3.911782499428444, 'rescued': 19, 'regressed': 7, 'original_cut': 115, 'edited_cut': 98}gsm8k {'n': 64, 'original_correct': 11, 'edited_correct': 21, 'gain_pp': 15.625, 'se_pp': 6.762242718751049, 'rescued': 15, 'regressed': 5, 'original_cut': 55, 'edited_cut': 42}math {'n': 64, 'original_correct': 5, 'edited_correct': 7, 'gain_pp': 3.125, 'se_pp': 3.837439570901079, 'rescued': 4, 'regressed': 2, 'original_cut': 60, 'edited_cut': 56}original {'tail_repeat': 0.8594810421113078, 'high_repeat': 111, 'finished_correct': 13, 'finished_correct_median_tokens': 4673.0}strength1 {'tail_repeat': 0.794828829553714, 'high_repeat': 95, 'finished_correct': 28, 'finished_correct_median_tokens': 11660.0}original repetition projections [(8, 0.307), (12, 0.394), (16, 0.809), (20, 1.415), (23, 1.624)]edited repetition projections [(8, 0.247), (12, 0.188), (16, 0.19), (20, 0.235), (23, 0.377)]Rescue gsm8k_train_1101 tokens 18662 start: Here's a thinking process that leads to the solution:
1.  **Analyze the Request:**    *   Given in end:  all ages:*   Beckett = 12*   Olaf = 15*   Shannen = 13*   Jack = 31
Total Sum = 12 + 15 + 13 + 31Total Sum = 71
\boxed{71}Rescue gsm8k_train_1927 tokens 12617 start: Here's a thinking process that leads to the solution:
1.  **Analyze the Problem:**    *   **Goal:* end: mphlets.***   Total for Mike = 5800.*   Total for Leo = 3600.*   Total combined = $5800 + 3600 = 9400$ pamphlets.
\boxed{9400}Rescue gsm8k_train_4960 tokens 4760 start: Here's a thinking process that leads to the suggested answer:
1.  **Analyze the Request:**    *    end: $$\text{Tip Amount} = 40 \times 0.10$$$$\text{Tip Amount} = 4$$
Therefore, the waiter should receive $4 in gratuity.
\boxed{4}Strength 1.5 saved; tensor count: 30Additional development IDs: ["gsm8k_train_3553","gsm8k_train_5789","gsm8k_train_1381","gsm8k_train_5240","gsm8k_train_494","gsm8k_train_422","gsm8k_train_462","gsm8k_train_2739","gsm8k_train_0","gsm8k_train_350","gsm8k_train_5000","gsm8k_train_4015","gsm8k_train_900","gsm8k_train_2723","gsm8k_train_4875","gsm8k_train_4620","gsm8k_train_817","gsm8k_train_7375","gsm8k_train_1451","gsm8k_train_5547","gsm8k_train_6247","gsm8k_train_4082","gsm8k_train_297","gsm8k_train_4799","gsm8k_train_6726","gsm8k_train_1016","gsm8k_train_1993","gsm8k_train_5910","gsm8k_train_2353","gsm8k_train_6380","gsm8k_train_1700","gsm8k_train_4276","math_train_geometry_350","math_train_intermediate_algebra_1136","math_train_algebra_603","math_train_counting_and_probability_685","math_train_geometry_67","math_train_prealgebra_321","math_train_algebra_469","math_train_counting_and_probability_483","math_train_counting_and_probability_644","math_train_algebra_209","math_train_intermediate_algebra_732","math_train_algebra_1097","math_train_intermediate_algebra_458","math_train_prealgebra_1170","math_train_number_theory_425","math_train_counting_and_probability_666","math_train_precalculus_176","math_train_intermediate_algebra_776","math_train_counting_and_probability_278","math_train_algebra_1454","math_train_geometry_837","math_train_counting_and_probability_181","math_train_intermediate_algebra_225","math_train_geometry_481","math_train_algebra_1017","math_train_geometry_249","math_train_prealgebra_38","math_train_algebra_244","math_train_geometry_642","math_train_intermediate_algebra_1243","math_train_number_theory_762","math_train_prealgebra_1153","math_train_geometry_618","math_train_prealgebra_618","math_train_intermediate_algebra_700","math_train_prealgebra_486","math_train_counting_and_probability_705","math_train_intermediate_algebra_1137","math_train_precalculus_565","math_train_algebra_720","math_train_intermediate_algebra_865","math_train_algebra_791","math_train_counting_and_probability_433","math_train_prealgebra_118","math_train_geometry_223","math_train_algebra_1108","math_train_precalculus_659","math_train_algebra_6","math_train_prealgebra_1029","math_train_geometry_18","math_train_number_theory_508","math_train_algebra_993","math_train_intermediate_algebra_742","math_train_geometry_82","math_train_intermediate_algebra_693","math_train_geometry_679","math_train_prealgebra_32","math_train_counting_and_probability_478","math_train_algebra_340","math_train_precalculus_41","math_train_counting_and_probability_119","math_train_algebra_426","math_train_geometry_440","math_train_geometry_289","math_train_counting_and_probability_3","math_train_geometry_724","math_train_counting_and_probability_254","math_train_intermediate_algebra_250","math_train_prealgebra_1035","math_train_algebra_962","math_train_prealgebra_111","math_train_counting_and_probability_683","math_train_counting_and_probability_648","math_train_algebra_21","math_train_algebra_292","math_train_algebra_1094","math_train_algebra_824","math_train_algebra_8","math_train_counting_and_probability_385","math_train_algebra_975","math_train_algebra_1243","math_train_algebra_1074","math_train_counting_and_probability_570","math_train_algebra_1105","math_train_algebra_1551","math_train_counting_and_probability_304","math_train_prealgebra_1025","math_train_prealgebra_955","math_train_prealgebra_672","math_train_number_theory_775","math_train_prealgebra_502","math_train_geometry_614","math_train_intermediate_algebra_638","math_train_precalculus_531","math_train_geometry_693","math_train_algebra_1190"]

The successful edit reduced the late-layer repetition projection from 1.624 to 0.377 and lowered tail repetition from 0.859 to 0.795. Rescued answers still contained substantial reasoning: completed correct answers had a median length of 11,660 tokens, so this is not simply forcing an immediate answer. The strength-1 weights are backed up. I will now test strength 1.5 on 256 paired problems to measure a stronger dose with greater statistical power.

forward

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ath_train_intermediate_algebra_865","math_train_algebra_791","math_train_counting_and_probability_433","math_train_prealgebra_118","math_train_geometry_223","math_train_algebra_1108","math_train_precalculus_659","math_train_algebra_6","math_train_prealgebra_1029","math_train_geometry_18","math_train_number_theory_508","math_train_algebra_993","math_train_intermediate_algebra_742","math_train_geometry_82","math_train_intermediate_algebra_693","math_train_geometry_679","math_train_prealgebra_32","math_train_counting_and_probability_478","math_train_algebra_340","math_train_precalculus_41","math_train_counting_and_probability_119","math_train_algebra_426","math_train_geometry_440","math_train_geometry_289","math_train_counting_and_probability_3","math_train_geometry_724","math_train_counting_and_probability_254","math_train_intermediate_algebra_250","math_train_prealgebra_1035","math_train_algebra_962","math_train_prealgebra_111","math_train_counting_and_probability_683","math_train_counting_and_probability_648","math_train_algebra_21","math_train_algebra_292","math_train_algebra_1094","math_train_algebra_824","math_train_algebra_8","math_train_counting_and_probability_385","math_train_algebra_975","math_train_algebra_1243","math_train_algebra_1074","math_train_counting_and_probability_570","math_train_algebra_1105","math_train_algebra_1551","math_train_counting_and_probability_304","math_train_prealgebra_1025","math_train_prealgebra_955","math_train_prealgebra_672","math_train_number_theory_775","math_train_prealgebra_502","math_train_geometry_614","math_train_intermediate_algebra_638","math_train_precalculus_531","math_train_geometry_693","math_train_algebra_1190"],"n":1,"max_tokens":32768,"activations":{"layers":[8,12,16,20,23],"mlp":false}}

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Forward pass 8 of 10 done (generate); results in /work/gen_9_original.jsonl and /work/gen_9_edited.jsonl. Summary:

{ "ok": true, "type": "generate", "model": "both", "files": {  "original": "/work/gen_9_original.jsonl",  "edited": "/work/gen_9_edited.jsonl" }, "activations": {  "original": {   "dir": "/work/gen_9_original_acts/",   "files": {    "hidden_last_prompt.npy": [     256,     5,     1024    ],    "hidden_mean_answer.npy": [     256,     5,     1024    ]   }  },  "edited": {   "dir": "/work/gen_9_edited_acts/",   "files": {    "hidden_last_prompt.npy": [     256,     5,     1024    ],    "hidden_mean_answer.npy": [     256,     5,     1024    ]   }  } }, "original": {  "generations": 256,  "graded": 256,  "accuracy": 0.08984375,  "accuracy_by_source": {   "gsm8k": 0.1146,   "math": 0.075  },  "mean_tokens": 30433,  "cut_share": 0.9141,  "failures": {   "wrong_answer": 0,   "no_answer_finished": 0,   "cut_at_max_tokens": 233  } }, "edited": {  "generations": 256,  "graded": 256,  "accuracy": 0.18359375,  "accuracy_by_source": {   "gsm8k": 0.3021,   "math": 0.1125  },  "mean_tokens": 28409,  "cut_share": 0.8203,  "failures": {   "wrong_answer": 2,   "no_answer_finished": 2,   "cut_at_max_tokens": 205  } }, "paired": {  "items": 256,  "accuracy_difference": 0.0938,  "standard_error": 0.0253,  "samples_wrong_to_right": 34,  "samples_right_to_wrong": 10 }, "s": 1309.7}

{ "ok": true, "type": "generate", "model": "both", "files": {  "original": "/work/gen_9_original.jsonl",  "edited": "/work/gen_9_edited.jsonl" }, "activations": {  "original": {   "dir": "/work/gen_9_original_acts/",   "files": {    "hidden_last_prompt.npy": [     256,     5,     1024    ],    "hidden_mean_answer.npy": [     256,     5,     1024    ]   }  },  "edited": {   "dir": "/work/gen_9_edited_acts/",   "files": {    "hidden_last_prompt.npy": [     256,     5,     1024    ],    "hidden_mean_answer.npy": [     256,     5,     1024    ]   }  } }, "original": {  "generations": 256,  "graded": 256,  "accuracy": 0.08984375,  "accuracy_by_source": {   "gsm8k": 0.1146,   "math": 0.075  },  "mean_tokens": 30433,  "cut_share": 0.9141,  "failures": {   "wrong_answer": 0,   "no_answer_finished": 0,   "cut_at_max_tokens": 233  } }, "edited": {  "generations": 256,  "graded": 256,  "accuracy": 0.18359375,  "accuracy_by_source": {   "gsm8k": 0.3021,   "math": 0.1125  },  "mean_tokens": 28409,  "cut_share": 0.8203,  "failures": {   "wrong_answer": 2,   "no_answer_finished": 2,   "cut_at_max_tokens": 205  } }, "paired": {  "items": 256,  "accuracy_difference": 0.0938,  "standard_error": 0.0253,  "samples_wrong_to_right": 34,  "samples_right_to_wrong": 10 }, "s": 1309.7}

Forward passes left: 2.

Strength 1.5 improved accuracy by 9.4 points on 256 paired development problems, with a smaller standard error of 2.5 points. Both sources improved, although most of the gain came from GSM8K. I will compare the two strengths only on their shared 128 problems and examine the repetition measurements before freezing one candidate. The remaining two passes will then evaluate that fixed edit on 512 previously unused problems, rather than continue tuning.

python

# Hypothesis: the stronger directional intervention should suppress the repetition state further; strength selection must use only development data.import json,os,shutil,numpy as npdef read_samples(path):    rs=[json.loads(line) for line in open(path)]    return [dict(s,id=r["id"],source=r["source"],sample=j) for r in rs for j,s in enumerate(r["samples"])]def summarize_pair(a,b):    aa={(s["id"],s["sample"]):s for s in a}    bb={(s["id"],s["sample"]):s for s in b}    keys=sorted(set(aa)&set(bb))    x=np.array([aa[k]["correct"] for k in keys],float)    y=np.array([bb[k]["correct"] for k in keys],float)    d=y-x    return dict(n=len(keys),a_correct=int(x.sum()),b_correct=int(y.sum()),                gain_pp=round(float(100*d.mean()),3),                se_pp=round(float(100*d.std(ddof=1)/np.sqrt(len(d))),3),                rescued=int((d==1).sum()),regressed=int((d==-1).sum()),                a_cut=sum(aa[k]["cut"] for k in keys),b_cut=sum(bb[k]["cut"] for k in keys))s8o=read_samples("/work/gen_8_original.jsonl");s8e=read_samples("/work/gen_8_edited.jsonl")s9o=read_samples("/work/gen_9_original.jsonl");s9e=read_samples("/work/gen_9_edited.jsonl")shared={s["id"] for s in s8e}print("Strength1.5 all256:",summarize_pair(s9o,s9e))print("Strength1.5 shared128 vs original:",summarize_pair([s for s in s9o if s["id"] in shared],s9e))print("Strength1 -> strength1.5 shared128:",summarize_pair(s8e,s9e))for src in ["gsm8k","math"]:    print(src,"strength1.5 all:",summarize_pair([s for s in s9o if s["source"]==src],[s for s in s9e if s["source"]==src]))    print(src,"strength1 -> 1.5:",summarize_pair([s for s in s8e if s["source"]==src],[s for s in s9e if s["source"]==src]))z=np.load("/work/repetition_directions.npz");directions={int(k):z[k] for k in z.files}for k,tag in [(8,"strength1"),(9,"strength1.5")]:    idx=json.load(open(f"/work/gen_{k}_edited_acts/index.json"))    h=np.load(f"/work/gen_{k}_edited_acts/hidden_mean_answer.npy").astype(np.float32)    keep=[i for i,r in enumerate(idx["samples"]) if r["item"] in shared]    print(tag,"shared128 projections",[(l,round(float(np.mean(h[keep,j]@directions[l])),3)) for j,l in enumerate(idx["layers"])])for tag,ss in [("strength1",s8e),("strength1.5",[s for s in s9e if s["id"] in shared])]:    fracs=[]    for s in ss:        ids=tok.encode(s["text"]).ids[-2048:]        grams=[tuple(ids[j:j+12]) for j in range(max(0,len(ids)-11))]        fracs.append(1-len(set(grams))/len(grams) if grams else 0.)    print(tag,"tail repetition",round(float(np.mean(fracs)),4),"high repetition",sum(x>0.8 for x in fracs),          "mean tokens",round(float(np.mean([s["tokens"] for s in ss]))))shutil.copytree("/work/edited","/work/best_rep_strength1p5",dirs_exist_ok=True)with open("/work/notes.md","a") as f:    f.write("\nPass8 (gen_9) strength1.5 on256 development: "+str(summarize_pair(s9o,s9e))+            "; same128 strength1->1.5 "+str(summarize_pair(s8e,s9e))+            ". Backup /work/best_rep_strength1p5. Two remaining passes reserved for fixed-candidate independent validation, 256 pairs each.\n")

# Hypothesis: the stronger directional intervention should suppress the repetition state further; strength selection must use only development data.import json,os,shutil,numpy as npdef read_samples(path):    rs=[json.loads(line) for line in open(path)]    return [dict(s,id=r["id"],source=r["source"],sample=j) for r in rs for j,s in enumerate(r["samples"])]def summarize_pair(a,b):    aa={(s["id"],s["sample"]):s for s in a}    bb={(s["id"],s["sample"]):s for s in b}    keys=sorted(set(aa)&set(bb))    x=np.array([aa[k]["correct"] for k in keys],float)    y=np.array([bb[k]["correct"] for k in keys],float)    d=y-x    return dict(n=len(keys),a_correct=int(x.sum()),b_correct=int(y.sum()),                gain_pp=round(float(100*d.mean()),3),                se_pp=round(float(100*d.std(ddof=1)/np.sqrt(len(d))),3),                rescued=int((d==1).sum()),regressed=int((d==-1).sum()),                a_cut=sum(aa[k]["cut"] for k in keys),b_cut=sum(bb[k]["cut"] for k in keys))s8o=read_samples("/work/gen_8_original.jsonl");s8e=read_samples("/work/gen_8_edited.jsonl")s9o=read_samples("/work/gen_9_original.jsonl");s9e=read_samples("/work/gen_9_edited.jsonl")shared={s["id"] for s in s8e}print("Strength1.5 all256:",summarize_pair(s9o,s9e))print("Strength1.5 shared128 vs original:",summarize_pair([s for s in s9o if s["id"] in shared],s9e))print("Strength1 -> strength1.5 shared128:",summarize_pair(s8e,s9e))for src in ["gsm8k","math"]:    print(src,"strength1.5 all:",summarize_pair([s for s in s9o if s["source"]==src],[s for s in s9e if s["source"]==src]))    print(src,"strength1 -> 1.5:",summarize_pair([s for s in s8e if s["source"]==src],[s for s in s9e if s["source"]==src]))z=np.load("/work/repetition_directions.npz");directions={int(k):z[k] for k in z.files}for k,tag in [(8,"strength1"),(9,"strength1.5")]:    idx=json.load(open(f"/work/gen_{k}_edited_acts/index.json"))    h=np.load(f"/work/gen_{k}_edited_acts/hidden_mean_answer.npy").astype(np.float32)    keep=[i for i,r in enumerate(idx["samples"]) if r["item"] in shared]    print(tag,"shared128 projections",[(l,round(float(np.mean(h[keep,j]@directions[l])),3)) for j,l in enumerate(idx["layers"])])for tag,ss in [("strength1",s8e),("strength1.5",[s for s in s9e if s["id"] in shared])]:    fracs=[]    for s in ss:        ids=tok.encode(s["text"]).ids[-2048:]        grams=[tuple(ids[j:j+12]) for j in range(max(0,len(ids)-11))]        fracs.append(1-len(set(grams))/len(grams) if grams else 0.)    print(tag,"tail repetition",round(float(np.mean(fracs)),4),"high repetition",sum(x>0.8 for x in fracs),          "mean tokens",round(float(np.mean([s["tokens"] for s in ss]))))shutil.copytree("/work/edited","/work/best_rep_strength1p5",dirs_exist_ok=True)with open("/work/notes.md","a") as f:    f.write("\nPass8 (gen_9) strength1.5 on256 development: "+str(summarize_pair(s9o,s9e))+            "; same128 strength1->1.5 "+str(summarize_pair(s8e,s9e))+            ". Backup /work/best_rep_strength1p5. Two remaining passes reserved for fixed-candidate independent validation, 256 pairs each.\n")

OUTPUT of Python run 53 of at most 300:

Strength1.5 all256: {'n': 256, 'a_correct': 23, 'b_correct': 47, 'gain_pp': 9.375, 'se_pp': 2.529, 'rescued': 34, 'regressed': 10, 'a_cut': 234, 'b_cut': 210}Strength1.5 shared128 vs original: {'n': 128, 'a_correct': 11, 'b_correct': 30, 'gain_pp': 14.844, 'se_pp': 3.857, 'rescued': 23, 'regressed': 4, 'a_cut': 117, 'b_cut': 100}Strength1 -> strength1.5 shared128: {'n': 128, 'a_correct': 28, 'b_correct': 30, 'gain_pp': 1.562, 'se_pp': 4.148, 'rescued': 15, 'regressed': 13, 'a_cut': 98, 'b_cut': 100}gsm8k strength1.5 all: {'n': 96, 'a_correct': 11, 'b_correct': 29, 'gain_pp': 18.75, 'se_pp': 5.196, 'rescued': 23, 'regressed': 5, 'a_cut': 86, 'b_cut': 66}gsm8k strength1 -> 1.5: {'n': 64, 'a_correct': 21, 'b_correct': 21, 'gain_pp': 0.0, 'se_pp': 7.043, 'rescued': 10, 'regressed': 10, 'a_cut': 42, 'b_cut': 44}math strength1.5 all: {'n': 160, 'a_correct': 12, 'b_correct': 18, 'gain_pp': 3.75, 'se_pp': 2.49, 'rescued': 11, 'regressed': 5, 'a_cut': 148, 'b_cut': 144}math strength1 -> 1.5: {'n': 64, 'a_correct': 7, 'b_correct': 9, 'gain_pp': 3.125, 'se_pp': 4.437, 'rescued': 5, 'regressed': 3, 'a_cut': 56, 'b_cut': 56}strength1 shared128 projections [(8, 0.247), (12, 0.188), (16, 0.19), (20, 0.235), (23, 0.377)]strength1.5 shared128 projections [(8, 0.255), (12, 0.151), (16, 0.059), (20, 0.046), (23, 0.255)]strength1 tail repetition 0.7948 high repetition 95 mean tokens 27972strength1.5 tail repetition 0.8078 high repetition 100 mean tokens 27578

Strength1.5 all256: {'n': 256, 'a_correct': 23, 'b_correct': 47, 'gain_pp': 9.375, 'se_pp': 2.529, 'rescued': 34, 'regressed': 10, 'a_cut': 234, 'b_cut': 210}Strength1.5 shared128 vs original: {'n': 128, 'a_correct': 11, 'b_correct': 30, 'gain_pp': 14.844, 'se_pp': 3.857, 'rescued': 23, 'regressed': 4, 'a_cut': 117, 'b_cut': 100}Strength1 -> strength1.5 shared128: {'n': 128, 'a_correct': 28, 'b_correct': 30, 'gain_pp': 1.562, 'se_pp': 4.148, 'rescued': 15, 'regressed': 13, 'a_cut': 98, 'b_cut': 100}gsm8k strength1.5 all: {'n': 96, 'a_correct': 11, 'b_correct': 29, 'gain_pp': 18.75, 'se_pp': 5.196, 'rescued': 23, 'regressed': 5, 'a_cut': 86, 'b_cut': 66}gsm8k strength1 -> 1.5: {'n': 64, 'a_correct': 21, 'b_correct': 21, 'gain_pp': 0.0, 'se_pp': 7.043, 'rescued': 10, 'regressed': 10, 'a_cut': 42, 'b_cut': 44}math strength1.5 all: {'n': 160, 'a_correct': 12, 'b_correct': 18, 'gain_pp': 3.75, 'se_pp': 2.49, 'rescued': 11, 'regressed': 5, 'a_cut': 148, 'b_cut': 144}math strength1 -> 1.5: {'n': 64, 'a_correct': 7, 'b_correct': 9, 'gain_pp': 3.125, 'se_pp': 4.437, 'rescued': 5, 'regressed': 3, 'a_cut': 56, 'b_cut': 56}strength1 shared128 projections [(8, 0.247), (12, 0.188), (16, 0.19), (20, 0.235), (23, 0.377)]strength1.5 shared128 projections [(8, 0.255), (12, 0.151), (16, 0.059), (20, 0.046), (23, 0.255)]strength1 tail repetition 0.7948 high repetition 95 mean tokens 27972strength1.5 tail repetition 0.8078 high repetition 100 mean tokens 27578

Strength 1.5 suppresses the measured repetition direction further, but its accuracy advantage over strength 1 is inconclusive: only two additional correct answers on the shared 128 problems. Tail repetition also does not improve monotonically, so I will not claim that the stronger dose is proven superior. I will freeze strength 1.5 because it has the larger positive paired development test, and make no further adjustments during validation. I will now draw two disjoint, balanced holdouts and retain the edit only if their pooled accuracy gain exceeds twice its paired standard error, while checking for source-specific harm.

python

# Hypothesis: the fixed strength-1.5 repetition repair generalizes to unseen mathematics problems rather than exploiting development examples.import os,json,glob,hashlib,numpy as npseen=set()for path in glob.glob("/work/gen_*.jsonl"):    for line in open(path):        r=json.loads(line)        if "id" in r: seen.add(r["id"])rng=np.random.default_rng(2026102401)held_by_source={}for src,filename in [("gsm8k","gsm8k_train.jsonl"),("math","math_train.jsonl")]:    pool=[json.loads(line) for line in open("/practice/"+filename)]    eligible=[r for r in pool if r["id"] not in seen]    selected=rng.choice(len(eligible),256,replace=False)    held_by_source[src]=[eligible[int(i)]["id"] for i in selected]holdout_A=held_by_source["gsm8k"][:128]+held_by_source["math"][:128]holdout_B=held_by_source["gsm8k"][128:]+held_by_source["math"][128:]assert len(set(holdout_A+holdout_B))==512assert not (set(holdout_A+holdout_B)&seen)validation_plan=dict(strength=1.5,layers=list(range(8,23)),    intervention="W - 1.5*u*(u^T W), both attention and MLP output projections",    holdout_A=holdout_A,holdout_B=holdout_B,    retention_rule="Pooled paired accuracy gain > 2 SE; report source-specific results and check significant harm.",    no_further_tuning=True)with open("/work/validation_plan.json","w") as f: json.dump(validation_plan,f,indent=2)print("Frozen candidate tensors:",len(edited()),"unused holdouts:",len(holdout_A),len(holdout_B))print("Holdout A:",json.dumps(holdout_A,separators=(",",":")))with open("/work/notes.md","a") as f:    f.write("\nFROZEN candidate: strength1.5 rank-one repetition repair, layers8..22, 30 writer tensors. Strength1 versus1.5 accuracy difference inconclusive (+2/128, SE4.148pp); state suppression is dose-responsive. No further tuning. Pre-registered two disjoint unseen balanced holdouts, 256 each (128 GSM+128 MATH), /work/validation_plan.json. Retain only if pooled gain >2 paired SE and check source-specific harm. Two passes remain.\n")

# Hypothesis: the fixed strength-1.5 repetition repair generalizes to unseen mathematics problems rather than exploiting development examples.import os,json,glob,hashlib,numpy as npseen=set()for path in glob.glob("/work/gen_*.jsonl"):    for line in open(path):        r=json.loads(line)        if "id" in r: seen.add(r["id"])rng=np.random.default_rng(2026102401)held_by_source={}for src,filename in [("gsm8k","gsm8k_train.jsonl"),("math","math_train.jsonl")]:    pool=[json.loads(line) for line in open("/practice/"+filename)]    eligible=[r for r in pool if r["id"] not in seen]    selected=rng.choice(len(eligible),256,replace=False)    held_by_source[src]=[eligible[int(i)]["id"] for i in selected]holdout_A=held_by_source["gsm8k"][:128]+held_by_source["math"][:128]holdout_B=held_by_source["gsm8k"][128:]+held_by_source["math"][128:]assert len(set(holdout_A+holdout_B))==512assert not (set(holdout_A+holdout_B)&seen)validation_plan=dict(strength=1.5,layers=list(range(8,23)),    intervention="W - 1.5*u*(u^T W), both attention and MLP output projections",    holdout_A=holdout_A,holdout_B=holdout_B,    retention_rule="Pooled paired accuracy gain > 2 SE; report source-specific results and check significant harm.",    no_further_tuning=True)with open("/work/validation_plan.json","w") as f: json.dump(validation_plan,f,indent=2)print("Frozen candidate tensors:",len(edited()),"unused holdouts:",len(holdout_A),len(holdout_B))print("Holdout A:",json.dumps(holdout_A,separators=(",",":")))with open("/work/notes.md","a") as f:    f.write("\nFROZEN candidate: strength1.5 rank-one repetition repair, layers8..22, 30 writer tensors. Strength1 versus1.5 accuracy difference inconclusive (+2/128, SE4.148pp); state suppression is dose-responsive. No further tuning. Pre-registered two disjoint unseen balanced holdouts, 256 each (128 GSM+128 MATH), /work/validation_plan.json. Retain only if pooled gain >2 paired SE and check source-specific harm. Two passes remain.\n")

OUTPUT of Python run 54 of at most 300:

Frozen candidate tensors: 30 unused holdouts: 256 256Holdout A: ["gsm8k_train_4362","gsm8k_train_7392","gsm8k_train_3840","gsm8k_train_4794","gsm8k_train_5973","gsm8k_train_917","gsm8k_train_6997","gsm8k_train_6003","gsm8k_train_1618","gsm8k_train_1803","gsm8k_train_3665","gsm8k_train_6623","gsm8k_train_4598","gsm8k_train_1774","gsm8k_train_6261","gsm8k_train_253","gsm8k_train_4642","gsm8k_train_6153","gsm8k_train_3376","gsm8k_train_6859","gsm8k_train_511","gsm8k_train_5061","gsm8k_train_3105","gsm8k_train_7329","gsm8k_train_4029","gsm8k_train_1979","gsm8k_train_439","gsm8k_train_7007","gsm8k_train_1684","gsm8k_train_2923","gsm8k_train_2441","gsm8k_train_6727","gsm8k_train_250","gsm8k_train_1092","gsm8k_train_2820","gsm8k_train_2247","gsm8k_train_3591","gsm8k_train_4179","gsm8k_train_1892","gsm8k_train_4316","gsm8k_train_3074","gsm8k_train_1420","gsm8k_train_1494","gsm8k_train_4135","gsm8k_train_4218","gsm8k_train_5286","gsm8k_train_7464","gsm8k_train_5729","gsm8k_train_4493","gsm8k_train_6470","gsm8k_train_2871","gsm8k_train_1187","gsm8k_train_880","gsm8k_train_4676","gsm8k_train_3765","gsm8k_train_4237","gsm8k_train_7155","gsm8k_train_735","gsm8k_train_716","gsm8k_train_4572","gsm8k_train_5796","gsm8k_train_2427","gsm8k_train_4125","gsm8k_train_479","gsm8k_train_1923","gsm8k_train_6030","gsm8k_train_6456","gsm8k_train_3545","gsm8k_train_724","gsm8k_train_3242","gsm8k_train_2642","gsm8k_train_3368","gsm8k_train_4093","gsm8k_train_636","gsm8k_train_2223","gsm8k_train_5288","gsm8k_train_5116","gsm8k_train_2208","gsm8k_train_1730","gsm8k_train_1625","gsm8k_train_5329","gsm8k_train_1670","gsm8k_train_5092","gsm8k_train_109","gsm8k_train_4094","gsm8k_train_6961","gsm8k_train_6511","gsm8k_train_3385","gsm8k_train_3474","gsm8k_train_1649","gsm8k_train_1208","gsm8k_train_6525","gsm8k_train_1568","gsm8k_train_1184","gsm8k_train_7286","gsm8k_train_5224","gsm8k_train_2581","gsm8k_train_3562","gsm8k_train_7347","gsm8k_train_2148","gsm8k_train_6018","gsm8k_train_5544","gsm8k_train_3344","gsm8k_train_1019","gsm8k_train_1399","gsm8k_train_7029","gsm8k_train_1527","gsm8k_train_5623","gsm8k_train_2222","gsm8k_train_3976","gsm8k_train_148","gsm8k_train_74","gsm8k_train_4502","gsm8k_train_2507","gsm8k_train_4200","gsm8k_train_5458","gsm8k_train_49","gsm8k_train_2662","gsm8k_train_4609","gsm8k_train_5797","gsm8k_train_276","gsm8k_train_4559","gsm8k_train_2757","gsm8k_train_2644","gsm8k_train_6089","gsm8k_train_5388","gsm8k_train_4972","gsm8k_train_7470","math_train_intermediate_algebra_1084","math_train_precalculus_630","math_train_intermediate_algebra_351","math_train_precalculus_458","math_train_prealgebra_739","math_train_intermediate_algebra_609","math_train_intermediate_algebra_1267","math_train_number_theory_137","math_train_counting_and_probability_269","math_train_precalculus_372","math_train_algebra_130","math_train_counting_and_probability_547","math_train_geometry_290","math_train_intermediate_algebra_267","math_train_algebra_1715","math_train_algebra_752","math_train_prealgebra_152","math_train_precalculus_340","math_train_intermediate_algebra_1281","math_train_number_theory_234","math_train_algebra_847","math_train_geometry_134","math_train_geometry_625","math_train_geometry_272","math_train_intermediate_algebra_470","math_train_geometry_294","math_train_intermediate_algebra_1121","math_train_intermediate_algebra_1067","math_train_algebra_598","math_train_algebra_514","math_train_algebra_419","math_train_geometry_832","math_train_intermediate_algebra_677","math_train_prealgebra_250","math_train_prealgebra_711","math_train_number_theory_157","math_train_counting_and_probability_217","math_train_number_theory_418","math_train_intermediate_algebra_430","math_train_counting_and_probability_89","math_train_intermediate_algebra_468","math_train_geometry_374","math_train_geometry_656","math_train_precalculus_567","math_train_prealgebra_1022","math_train_prealgebra_1152","math_train_algebra_1344","math_train_prealgebra_1109","math_train_prealgebra_652","math_train_intermediate_algebra_215","math_train_precalculus_3","math_train_intermediate_algebra_502","math_train_intermediate_algebra_83","math_train_precalculus_200","math_train_precalculus_186","math_train_algebra_384","math_train_intermediate_algebra_547","math_train_number_theory_148","math_train_number_theory_688","math_train_intermediate_algebra_1186","math_train_counting_and_probability_58","math_train_precalculus_155","math_train_number_theory_526","math_train_algebra_1728","math_train_algebra_1181","math_train_algebra_44","math_train_number_theory_667","math_train_counting_and_probability_624","math_train_prealgebra_1113","math_train_prealgebra_534","math_train_prealgebra_1099","math_train_counting_and_probability_187","math_train_algebra_260","math_train_algebra_907","math_train_algebra_339","math_train_precalculus_162","math_train_algebra_330","math_train_intermediate_algebra_222","math_train_number_theory_483","math_train_algebra_1530","math_train_precalculus_618","math_train_counting_and_probability_68","math_train_numbe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Frozen candidate tensors: 30 unused holdouts: 256 256Holdout A: 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The candidate and retention rule are now fixed, and both holdouts exclude every previously generated practice problem. Each holdout contains 128 GSM8K and 128 MATH problems. I will evaluate the first 256 paired problems without changing any weights afterward. This tests whether the development gain and reduction in the repetition-associated state transfer to unseen questions.

forward

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[note from the experimenters] Corrections about the tools (no action needed if they do not concern you): (1) Attention probabilities ("attn_layers") work now; give full-attention layer indices (3, 7, 11, 15, 19, 23). (2) Separate generation passes do not reproduce each other exactly, even for the same request and weights (the server's batching changes the random numbers): compare edited and original only within one "model": "both" pass, and never repeat an identical request with unchanged edits (it costs a pass and adds little). (3) /work/eval_prompt_format.txt gives the exact evaluation prompt; to score a generated answer with a teacher-forced pass, send prompt + answer as one raw text in "texts" ("chats" re-renders assistant turns and drops their reasoning). (4) Before every pass, write to /work/notes.md the exact current edit (tensors, what was changed, strength) and the problem set, so that you can rebuild them if the conversation is restarted.

The forward-pass server did not answer in time; the request was not counted.

The validation request timed out and was not counted, so there is no new accuracy result to interpret. I will checkpoint the exact edit and holdout, verify that the weights are unchanged, and check whether the failed request left any result files before retrying. I will also treat comparisons between separate strength-testing runs as exploratory, since batching prevents identical random draws across passes. The acceptance decision will rely only on original-versus-edited comparisons within the two held-out paired runs.

python

# Hypothesis: the frozen repetition-direction edit should generalize; preserve its exact specification before retrying the uncounted validation request.import os,json,numpy as np,torchplan=json.load(open("/work/validation_plan.json"))z=np.load("/work/repetition_directions.npz")expected_names=[]max_error=0.for l in range(8,23):    u=torch.from_numpy(z[str(l+1)].copy()).float()    for suffix in ["mlp.down_proj.weight","self_attn.o_proj.weight" if l%4==3 else "linear_attn.out_proj.weight"]:        name=f"model.language_model.layers.{l}.{suffix}"        expected_names.append(name)        w0=load_original(name)        expected=w0-1.5*torch.outer(u,u@w0)        max_error=max(max_error,float((load(name)-expected).abs().max()))assert set(edited())==set(expected_names)print("Frozen-edit verification: tensors",len(expected_names),"maximum absolute reconstruction error",max_error)print("Possible timed-out result files:",[s for s in os.listdir("/work") if s.startswith("gen_10")])checkpoint=dict(type="generate",model="both",problems=plan["holdout_A"],n=1,max_tokens=[REDACTED]                activations=dict(layers=[12,20,23],mlp=False))with open("/work/next_forward_request.json","w") as f: json.dump(checkpoint,f,indent=2)with open("/work/notes.md","a") as f:    f.write("\nPRE-PASS CHECKPOINT: holdout A; prior request timed out, NOT counted. Still 8/10 passes used.\n")    f.write("Exact edited tensors: "+json.dumps(expected_names)+"\n")    f.write("Each W'=W_original-1.5*outer(u,u@W_original); u for layer l is key str(l+1) in /work/repetition_directions.npz. All 30 current tensors verified against reconstruction; max_abs_error="+str(max_error)+". No other edits.\n")    f.write("Exact problem set and request: "+json.dumps(checkpoint,separators=(",",":"))+"\n")    f.write("Frozen candidate strength1.5. No further tuning. Holdout B is fixed in validation_plan.json. Retain only pooled512 paired gain>2SE; check source harms. Prior development strength1:+9.375pp SE3.912 (128 pairs); strength1.5:+9.375pp SE2.529 (256 pairs). Separate passes have different RNG batching; only within-pass original/edited comparisons support claims. Failed MLP-neuron and copying-head edits were fully reverted. Final norm, embeddings, output layer untouched.\n")

# Hypothesis: the frozen repetition-direction edit should generalize; preserve its exact specification before retrying the uncounted validation request.import os,json,numpy as np,torchplan=json.load(open("/work/validation_plan.json"))z=np.load("/work/repetition_directions.npz")expected_names=[]max_error=0.for l in range(8,23):    u=torch.from_numpy(z[str(l+1)].copy()).float()    for suffix in ["mlp.down_proj.weight","self_attn.o_proj.weight" if l%4==3 else "linear_attn.out_proj.weight"]:        name=f"model.language_model.layers.{l}.{suffix}"        expected_names.append(name)        w0=load_original(name)        expected=w0-1.5*torch.outer(u,u@w0)        max_error=max(max_error,float((load(name)-expected).abs().max()))assert set(edited())==set(expected_names)print("Frozen-edit verification: tensors",len(expected_names),"maximum absolute reconstruction error",max_error)print("Possible timed-out result files:",[s for s in os.listdir("/work") if s.startswith("gen_10")])checkpoint=dict(type="generate",model="both",problems=plan["holdout_A"],n=1,max_tokens=[REDACTED]                activations=dict(layers=[12,20,23],mlp=False))with open("/work/next_forward_request.json","w") as f: json.dump(checkpoint,f,indent=2)with open("/work/notes.md","a") as f:    f.write("\nPRE-PASS CHECKPOINT: holdout A; prior request timed out, NOT counted. Still 8/10 passes used.\n")    f.write("Exact edited tensors: "+json.dumps(expected_names)+"\n")    f.write("Each W'=W_original-1.5*outer(u,u@W_original); u for layer l is key str(l+1) in /work/repetition_directions.npz. All 30 current tensors verified against reconstruction; max_abs_error="+str(max_error)+". No other edits.\n")    f.write("Exact problem set and request: "+json.dumps(checkpoint,separators=(",",":"))+"\n")    f.write("Frozen candidate strength1.5. No further tuning. Holdout B is fixed in validation_plan.json. Retain only pooled512 paired gain>2SE; check source harms. Prior development strength1:+9.375pp SE3.912 (128 pairs); strength1.5:+9.375pp SE2.529 (256 pairs). Separate passes have different RNG batching; only within-pass original/edited comparisons support claims. Failed MLP-neuron and copying-head edits were fully reverted. Final norm, embeddings, output layer untouched.\n")

OUTPUT of Python run 55 of at most 300:

Frozen-edit verification: tensors 30 maximum absolute reconstruction error 0.0Possible timed-out result files: []

Frozen-edit verification: tensors 30 maximum absolute reconstruction error 0.0Possible timed-out result files: []

All 30 edited tensors exactly match the frozen strength-1.5 specification, and the timed-out request left no result files. The complete edit and intended problem set are now recorded in the notes. I will retry holdout A unchanged; a positive paired accuracy gain would support generalization, while a null or negative result would count against retaining the edit.

forward

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The forward pass failed and was not counted: RuntimeError: generation worker failed: self.llm_engine = LLMEngine.from_engine_args( File "/llmrl/lib/python3.10/site-packages/vllm/v1/engine/llm_engine.py", line 184, in from_engine_args return cls( File "/llmrl/lib/python3.10/site-packages/vllm/v1/engine/llm_engine.py", line 109, in init self.engine_core = EngineCoreClient.make_client( File "/llmrl/lib/python3.10/site-packages/vllm/v1/engine/core_client.py", line 122, in make_client return SyncMPClient( File "/llmrl/lib/python3.10/site-packages/vllm/tracing/otel.py", line 178, in sync_wrapper return func(*args, **kwargs) File "/llmrl/lib/python3.10/site-packages/vllm/v1/engine/core_client.py", line 880, in init super().init( File "/llmrl/lib/python3.10/site-packages/vllm/v1/engine/core_client.py", line 656, in init with launch_core_engines( File "/.uv/python_install/cpython-3.10-linux-x86_64-gnu/lib/python3.10/contextlib.py", line 142, in exit next(self.gen) File "/llmrl/lib/python3.10/site-packages/vllm/v1/engine/utils.py", line 1240, in launch_core_engines wait_for_engine_startup( File "/llmrl/lib/python3.10/site-packages/vllm/v1/engine/utils.py", line 1320, in wait_for_engine_startup raise RuntimeError( RuntimeError: Engine core initialization failed. See root cause above. Failed core proc(s): {}

The retry failed during generation-engine initialization, before producing any model output, and again did not consume a pass. This is an infrastructure failure, not evidence about the edit’s quality. I will remove optional activation collection and retry the same frozen candidate and holdout using generation alone. The problems, decoding settings, and accuracy-based acceptance rule will remain unchanged.

python

# Hypothesis: optional instrumentation can be omitted without changing the held-out accuracy test; engine failures provide no model-quality evidence.import os,jsonplan=json.load(open("/work/validation_plan.json"))request=dict(type="generate",model="both",problems=plan["holdout_A"],n=1,max_tokens=[REDACTED]with open("/work/next_forward_request.json","w") as f: json.dump(request,f,indent=2)logfiles=[n for n in os.listdir("/work") if n.endswith(".log")]print("Work log files:",logfiles)print("New generation files:",[n for n in os.listdir("/work") if n.startswith(("gen_10","gen_11"))])print("Current edited tensor count:",len(edited()))with open("/work/notes.md","a") as f:    f.write("\nPRE-PASS RETRY: second holdout-A attempt failed at vLLM engine initialization, not counted; no results. Still8/10 used. Dropping optional activation collection only.\n")    f.write("Exact current edited tensor names: "+json.dumps(list(edited()))+"\n")    f.write("Unchanged strength1.5: for each writer in layers8..22, W'=W_original-1.5*outer(u,u@W_original), u=/work/repetition_directions.npz[str(layer+1)]. Reconstruction previously verified exact; final layer23 and forbidden tensors untouched.\n")    f.write("Exact next request: "+json.dumps(request,separators=(",",":"))+"\n")    f.write("Candidate frozen; holdout A and B preselected in validation_plan.json, each256 unseen balanced problems. Two uncounted infrastructure failures. Do not interpret failures as accuracy results. Retain only pooled held-out paired gain>2SE; no strength tuning from holdouts.\n")

# Hypothesis: optional instrumentation can be omitted without changing the held-out accuracy test; engine failures provide no model-quality evidence.import os,jsonplan=json.load(open("/work/validation_plan.json"))request=dict(type="generate",model="both",problems=plan["holdout_A"],n=1,max_tokens=[REDACTED]with open("/work/next_forward_request.json","w") as f: json.dump(request,f,indent=2)logfiles=[n for n in os.listdir("/work") if n.endswith(".log")]print("Work log files:",logfiles)print("New generation files:",[n for n in os.listdir("/work") if n.startswith(("gen_10","gen_11"))])print("Current edited tensor count:",len(edited()))with open("/work/notes.md","a") as f:    f.write("\nPRE-PASS RETRY: second holdout-A attempt failed at vLLM engine initialization, not counted; no results. Still8/10 used. Dropping optional activation collection only.\n")    f.write("Exact current edited tensor names: "+json.dumps(list(edited()))+"\n")    f.write("Unchanged strength1.5: for each writer in layers8..22, W'=W_original-1.5*outer(u,u@W_original), u=/work/repetition_directions.npz[str(layer+1)]. Reconstruction previously verified exact; final layer23 and forbidden tensors untouched.\n")    f.write("Exact next request: "+json.dumps(request,separators=(",",":"))+"\n")    f.write("Candidate frozen; holdout A and B preselected in validation_plan.json, each256 unseen balanced problems. Two uncounted infrastructure failures. Do not interpret failures as accuracy results. Retain only pooled held-out paired gain>2SE; no strength tuning from holdouts.\n")

OUTPUT of Python run 56 of at most 300:

There are no partial validation results or local error logs, and the 30-tensor candidate is still unchanged. I have checkpointed the generation-only request, preserving all 256 holdout-A problems and the full token limit. I will retry this simpler request now; only a completed paired evaluation will count as validation evidence.

forward

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Forward pass 9 of 10 done (generate); results in /work/gen_12_original.jsonl and /work/gen_12_edited.jsonl. Summary:

{ "ok": true, "type": "generate", "model": "both", "files": {  "original": "/work/gen_12_original.jsonl",  "edited": "/work/gen_12_edited.jsonl" }, "activations": null, "original": {  "generations": 256,  "graded": 256,  "accuracy": 0.09765625,  "accuracy_by_source": {   "gsm8k": 0.125,   "math": 0.0703  },  "mean_tokens": 30619,  "cut_share": 0.9219,  "failures": {   "wrong_answer": 0,   "no_answer_finished": 0,   "cut_at_max_tokens": 231  } }, "edited": {  "generations": 256,  "graded": 256,  "accuracy": 0.16796875,  "accuracy_by_source": {   "gsm8k": 0.2109,   "math": 0.125  },  "mean_tokens": 28826,  "cut_share": 0.8203,  "failures": {   "wrong_answer": 1,   "no_answer_finished": 2,   "cut_at_max_tokens": 210  } }, "paired": {  "items": 256,  "accuracy_difference": 0.0703,  "standard_error": 0.0244,  "samples_wrong_to_right": 29,  "samples_right_to_wrong": 11 }, "s": 3167.3}

{ "ok": true, "type": "generate", "model": "both", "files": {  "original": "/work/gen_12_original.jsonl",  "edited": "/work/gen_12_edited.jsonl" }, "activations": null, "original": {  "generations": 256,  "graded": 256,  "accuracy": 0.09765625,  "accuracy_by_source": {   "gsm8k": 0.125,   "math": 0.0703  },  "mean_tokens": 30619,  "cut_share": 0.9219,  "failures": {   "wrong_answer": 0,   "no_answer_finished": 0,   "cut_at_max_tokens": 231  } }, "edited": {  "generations": 256,  "graded": 256,  "accuracy": 0.16796875,  "accuracy_by_source": {   "gsm8k": 0.2109,   "math": 0.125  },  "mean_tokens": 28826,  "cut_share": 0.8203,  "failures": {   "wrong_answer": 1,   "no_answer_finished": 2,   "cut_at_max_tokens": 210  } }, "paired": {  "items": 256,  "accuracy_difference": 0.0703,  "standard_error": 0.0244,  "samples_wrong_to_right": 29,  "samples_right_to_wrong": 11 }, "s": 3167.3}

Forward passes left: 1.

The first unseen holdout improved from 9.8% to 16.8% accuracy: a paired gain of 7.0 points with a 2.4-point standard error. Both GSM8K and MATH improved, supporting transfer beyond the development problems. I will leave the weights unchanged, record this result, and prepare the preselected second holdout. The final decision will use all 512 held-out pairs, not this favorable first half alone.

python

# Hypothesis: the fixed edit's held-out gain will replicate on a second disjoint set; no tuning follows the first validation result.import json,numpy as npdef flat_now(path):    return [dict(s,id=r["id"],source=r["source"],sample=j)            for r in [json.loads(line) for line in open(path)] for j,s in enumerate(r["samples"])]def paired_now(a,b):    assert [(s["id"],s["sample"]) for s in a]==[(s["id"],s["sample"]) for s in b]    x=np.array([s["correct"] for s in a],float);y=np.array([s["correct"] for s in b],float);d=y-x    return dict(n=len(a),original_correct=int(x.sum()),edited_correct=int(y.sum()),                gain_pp=float(100*d.mean()),se_pp=float(100*d.std(ddof=1)/np.sqrt(len(d))),                rescued=int((d==1).sum()),regressed=int((d==-1).sum()),                original_cut=sum(s["cut"] for s in a),edited_cut=sum(s["cut"] for s in b))a12=flat_now("/work/gen_12_original.jsonl");b12=flat_now("/work/gen_12_edited.jsonl")holdA_result=paired_now(a12,b12)print("Holdout A:",holdA_result)for src in ["gsm8k","math"]:    print(src,paired_now([s for s in a12 if s["source"]==src],[s for s in b12 if s["source"]==src]))plan=json.load(open("/work/validation_plan.json"))assert {s["id"] for s in a12}==set(plan["holdout_A"])assert not(set(plan["holdout_A"])&set(plan["holdout_B"]))request=dict(type="generate",model="both",problems=plan["holdout_B"],n=1,max_tokens=[REDACTED]with open("/work/next_forward_request.json","w") as f: json.dump(request,f,indent=2)with open("/work/notes.md","a") as f:    f.write("\nPass9 (gen_12), independent holdout A, frozen strength1.5: "+str(holdA_result)+            ". GSM16->27/128; MATH9->16/128. No edits or selection changes. Last pass reserved for preselected holdout B.\n")    f.write("PRE-PASS10 exact current tensors: "+json.dumps(list(edited()))+"\n")    f.write("Unchanged W'=W_original-1.5*outer(u,u@W_original); layer l uses /work/repetition_directions.npz[str(l+1)]. Writers: MLP down_proj and attention output, layers8..22. All other tensors untouched.\n")    f.write("Exact next request: "+json.dumps(request,separators=(",",":"))+"\n")    f.write("Final retention rule remains pooled512 unseen paired gain>2SE, check source-specific harm. No adaptive tuning. Holdout A positive7.03125pp SE2.43583pp; both sources positive. Final choice only after holdout B.\n")print("Holdout B:",json.dumps(plan["holdout_B"],separators=(",",":")))

# Hypothesis: the fixed edit's held-out gain will replicate on a second disjoint set; no tuning follows the first validation result.import json,numpy as npdef flat_now(path):    return [dict(s,id=r["id"],source=r["source"],sample=j)            for r in [json.loads(line) for line in open(path)] for j,s in enumerate(r["samples"])]def paired_now(a,b):    assert [(s["id"],s["sample"]) for s in a]==[(s["id"],s["sample"]) for s in b]    x=np.array([s["correct"] for s in a],float);y=np.array([s["correct"] for s in b],float);d=y-x    return dict(n=len(a),original_correct=int(x.sum()),edited_correct=int(y.sum()),                gain_pp=float(100*d.mean()),se_pp=float(100*d.std(ddof=1)/np.sqrt(len(d))),                rescued=int((d==1).sum()),regressed=int((d==-1).sum()),                original_cut=sum(s["cut"] for s in a),edited_cut=sum(s["cut"] for s in b))a12=flat_now("/work/gen_12_original.jsonl");b12=flat_now("/work/gen_12_edited.jsonl")holdA_result=paired_now(a12,b12)print("Holdout A:",holdA_result)for src in ["gsm8k","math"]:    print(src,paired_now([s for s in a12 if s["source"]==src],[s for s in b12 if s["source"]==src]))plan=json.load(open("/work/validation_plan.json"))assert {s["id"] for s in a12}==set(plan["holdout_A"])assert not(set(plan["holdout_A"])&set(plan["holdout_B"]))request=dict(type="generate",model="both",problems=plan["holdout_B"],n=1,max_tokens=[REDACTED]with open("/work/next_forward_request.json","w") as f: json.dump(request,f,indent=2)with open("/work/notes.md","a") as f:    f.write("\nPass9 (gen_12), independent holdout A, frozen strength1.5: "+str(holdA_result)+            ". GSM16->27/128; MATH9->16/128. No edits or selection changes. Last pass reserved for preselected holdout B.\n")    f.write("PRE-PASS10 exact current tensors: "+json.dumps(list(edited()))+"\n")    f.write("Unchanged W'=W_original-1.5*outer(u,u@W_original); layer l uses /work/repetition_directions.npz[str(l+1)]. Writers: MLP down_proj and attention output, layers8..22. All other tensors untouched.\n")    f.write("Exact next request: "+json.dumps(request,separators=(",",":"))+"\n")    f.write("Final retention rule remains pooled512 unseen paired gain>2SE, check source-specific harm. No adaptive tuning. Holdout A positive7.03125pp SE2.43583pp; both sources positive. Final choice only after holdout B.\n")print("Holdout B:",json.dumps(plan["holdout_B"],separators=(",",":")))

OUTPUT of Python run 57 of at most 300:

Holdout A: {'n': 256, 'original_correct': 25, 'edited_correct': 43, 'gain_pp': 7.03125, 'se_pp': 2.435892978296806, 'rescued': 29, 'regressed': 11, 'original_cut': 236, 'edited_cut': 210}gsm8k {'n': 128, 'original_correct': 16, 'edited_correct': 27, 'gain_pp': 8.59375, 'se_pp': 4.154281029675727, 'rescued': 20, 'regressed': 9, 'original_cut': 117, 'edited_cut': 99}math {'n': 128, 'original_correct': 9, 'edited_correct': 16, 'gain_pp': 5.46875, 'se_pp': 2.5556295331153724, 'rescued': 9, 'regressed': 2, 'original_cut': 119, 'edited_cut': 111}Holdout B: 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Holdout A: {'n': 256, 'original_correct': 25, 'edited_correct': 43, 'gain_pp': 7.03125, 'se_pp': 2.435892978296806, 'rescued': 29, 'regressed': 11, 'original_cut': 236, 'edited_cut': 210}gsm8k {'n': 128, 'original_correct': 16, 'edited_correct': 27, 'gain_pp': 8.59375, 'se_pp': 4.154281029675727, 'rescued': 20, 'regressed': 9, 'original_cut': 117, 'edited_cut': 99}math {'n': 128, 'original_correct': 9, 'edited_correct': 16, 'gain_pp': 5.46875, 'se_pp': 2.5556295331153724, 'rescued': 9, 'regressed': 2, 'original_cut': 119, 'edited_cut': 111}Holdout B: 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Holdout A’s gains were 11 additional correct GSM8K answers and seven additional correct MATH answers, with no weight changes afterward. The second holdout is disjoint and was selected before seeing those results. I will now run the final 256 paired problems with the same frozen edit. Replication would strengthen the case for a general repetition repair; a sufficiently negative result could still overturn the pooled acceptance decision.

forward

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The second holdout failed at engine startup and was not counted, so its outcome remains unknown. I will keep the candidate and problem set unchanged, record the infrastructure failure, and allow a short pause before retrying. This avoids spending the last pass on a smaller diagnostic run or changing the validation plan in response to a server error.

python

# Hypothesis: the startup failure is transient infrastructure trouble; retrying the unchanged holdout preserves the validation design.import os,json,timeplan=json.load(open("/work/validation_plan.json"))request=dict(type="generate",model="both",problems=plan["holdout_B"],n=1,max_tokens=[REDACTED]print("Possible holdout-B files:",[n for n in os.listdir("/work") if n.startswith("gen_13")])print("Frozen edited tensors:",len(edited()))with open("/work/notes.md","a") as f:    f.write("\nPRE-PASS10 RETRY: holdout B failed during engine initialization, uncounted. Still9/10 successful passes. Holdout A remains25->43 correct/256, +7.03125pp SE2.435893. No model updates.\n")    f.write("Exact current tensors: "+json.dumps(list(edited()))+"\n")    f.write("Exact edit: W'=W_original-1.5*outer(u,u@W_original), u from /work/repetition_directions.npz[str(layer+1)], attention and MLP writer matrices in layers8..22. All others unchanged.\n")    f.write("Exact retry request: "+json.dumps(request,separators=(",",":"))+"\n")    f.write("Frozen preselected validation remains512 total unseen pairs. No tuning. Waiting60 seconds for worker cleanup before unchanged retry; failure supplies no accuracy evidence.\n")time.sleep(60)print("Pause complete; retry the same holdout B.")

# Hypothesis: the startup failure is transient infrastructure trouble; retrying the unchanged holdout preserves the validation design.import os,json,timeplan=json.load(open("/work/validation_plan.json"))request=dict(type="generate",model="both",problems=plan["holdout_B"],n=1,max_tokens=[REDACTED]print("Possible holdout-B files:",[n for n in os.listdir("/work") if n.startswith("gen_13")])print("Frozen edited tensors:",len(edited()))with open("/work/notes.md","a") as f:    f.write("\nPRE-PASS10 RETRY: holdout B failed during engine initialization, uncounted. Still9/10 successful passes. Holdout A remains25->43 correct/256, +7.03125pp SE2.435893. No model updates.\n")    f.write("Exact current tensors: "+json.dumps(list(edited()))+"\n")    f.write("Exact edit: W'=W_original-1.5*outer(u,u@W_original), u from /work/repetition_directions.npz[str(layer+1)], attention and MLP writer matrices in layers8..22. All others unchanged.\n")    f.write("Exact retry request: "+json.dumps(request,separators=(",",":"))+"\n")    f.write("Frozen preselected validation remains512 total unseen pairs. No tuning. Waiting60 seconds for worker cleanup before unchanged retry; failure supplies no accuracy evidence.\n")time.sleep(60)print("Pause complete; retry the same holdout B.")

OUTPUT of Python run 58 of at most 300:

The pause is complete, no partial holdout-B results were written, and all 30 edited tensors remain in place. The exact retry specification is recorded in the notes. I will rerun the same 256 paired problems and use the completed result together with holdout A for the final retention decision.

forward

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Forward pass 10 of 10 done (generate); results in /work/gen_14_original.jsonl and /work/gen_14_edited.jsonl. Summary:

{ "ok": true, "type": "generate", "model": "both", "files": {  "original": "/work/gen_14_original.jsonl",  "edited": "/work/gen_14_edited.jsonl" }, "activations": null, "original": {  "generations": 256,  "graded": 256,  "accuracy": 0.14453125,  "accuracy_by_source": {   "gsm8k": 0.1953,   "math": 0.0938  },  "mean_tokens": 28831,  "cut_share": 0.8555,  "failures": {   "wrong_answer": 1,   "no_answer_finished": 0,   "cut_at_max_tokens": 218  } }, "edited": {  "generations": 256,  "graded": 256,  "accuracy": 0.1484375,  "accuracy_by_source": {   "gsm8k": 0.1641,   "math": 0.1328  },  "mean_tokens": 28906,  "cut_share": 0.8242,  "failures": {   "wrong_answer": 6,   "no_answer_finished": 1,   "cut_at_max_tokens": 211  } }, "paired": {  "items": 256,  "accuracy_difference": 0.0039,  "standard_error": 0.0263,  "samples_wrong_to_right": 23,  "samples_right_to_wrong": 22 }, "s": 1137.4}

{ "ok": true, "type": "generate", "model": "both", "files": {  "original": "/work/gen_14_original.jsonl",  "edited": "/work/gen_14_edited.jsonl" }, "activations": null, "original": {  "generations": 256,  "graded": 256,  "accuracy": 0.14453125,  "accuracy_by_source": {   "gsm8k": 0.1953,   "math": 0.0938  },  "mean_tokens": 28831,  "cut_share": 0.8555,  "failures": {   "wrong_answer": 1,   "no_answer_finished": 0,   "cut_at_max_tokens": 218  } }, "edited": {  "generations": 256,  "graded": 256,  "accuracy": 0.1484375,  "accuracy_by_source": {   "gsm8k": 0.1641,   "math": 0.1328  },  "mean_tokens": 28906,  "cut_share": 0.8242,  "failures": {   "wrong_answer": 6,   "no_answer_finished": 1,   "cut_at_max_tokens": 211  } }, "paired": {  "items": 256,  "accuracy_difference": 0.0039,  "standard_error": 0.0263,  "samples_wrong_to_right": 23,  "samples_right_to_wrong": 22 }, "s": 1137.4}

Forward passes left: 0. All forward passes are used. Make your final choice of edits (Python is still available), then reply without a code block.

The second holdout was essentially neutral: one additional correct answer, a gain of 0.4 points with a 2.6-point standard error. That weakens the evidence compared with the first holdout, so I will not describe the improvement as strongly replicated. I will now calculate the prespecified pooled result, check both sources, and verify the final tensors against the frozen formula. The edit will be retained only if it meets the stated pooled threshold; the final report will explicitly note the uncertainty.

python

# Hypothesis: the frozen candidate's pooled held-out accuracy gain meets the prespecified retention rule despite a neutral second holdout.import os,json,numpy as np,torch,hashlib,shutilfrom scipy.stats import binomtestdef read_final(path):    records=[json.loads(line) for line in open(path)]    return [dict(s,id=r["id"],source=r["source"],sample=j) for r in records for j,s in enumerate(r["samples"])]def final_stats(a,b):    assert [(s["id"],s["sample"]) for s in a]==[(s["id"],s["sample"]) for s in b]    x=np.array([s["correct"] for s in a],float)    y=np.array([s["correct"] for s in b],float)    d=y-x; gain=float(d.mean());se=float(d.std(ddof=1)/np.sqrt(len(d)))    rescued=int((d==1).sum());regressed=int((d==-1).sum())    return dict(n=len(d),original_correct=int(x.sum()),edited_correct=int(y.sum()),                original_accuracy=float(x.mean()),edited_accuracy=float(y.mean()),                gain_pp=100*gain,se_pp=100*se,                normal_95ci_pp=[100*(gain-1.96*se),100*(gain+1.96*se)],                rescued=rescued,regressed=regressed,                exact_mcnemar_p=float(binomtest(rescued,rescued+regressed,.5).pvalue),                original_cut=sum(s["cut"] for s in a),edited_cut=sum(s["cut"] for s in b),                original_mean_tokens=[REDACTED] for s in a])),                edited_mean_tokens=[REDACTED] for s in b])))oa=read_final("/work/gen_12_original.jsonl");ea=read_final("/work/gen_12_edited.jsonl")ob=read_final("/work/gen_14_original.jsonl");eb=read_final("/work/gen_14_edited.jsonl")plan=json.load(open("/work/validation_plan.json"))assert {s["id"] for s in oa}==set(plan["holdout_A"])assert {s["id"] for s in ob}==set(plan["holdout_B"])assert not({s["id"] for s in oa}&{s["id"] for s in ob})original,modified=oa+ob,ea+ebresults={"holdout_A":final_stats(oa,ea),"holdout_B":final_stats(ob,eb),         "pooled":final_stats(original,modified)}for src in ["gsm8k","math"]:    results[src]=final_stats([s for s in original if s["source"]==src],                             [s for s in modified if s["source"]==src])for k,v in results.items(): print(k,v)p=results["pooled"]retain=p["gain_pp"]>2*p["se_pp"] and all(results[s]["gain_pp"]>-2*results[s]["se_pp"] for s in ["gsm8k","math"])print("Prespecified retention threshold met:",retain,"gain/SE:",round(p["gain_pp"]/p["se_pp"],3))behavior={}for label,ss in [("original",original),("edited",modified)]:    fractions=[]    for s in ss:        ids=tok.encode(s["text"]).ids[-2048:]        grams=[tuple(ids[i:i+12]) for i in range(max(0,len(ids)-11))]        fractions.append(1-len(set(grams))/len(grams) if grams else 0.)    finished=[s["tokens"] for s in ss if s["correct"] and not s["cut"]]    behavior[label]=dict(mean_tail_repetition=float(np.mean(fractions)),                         finished_correct=len(finished),                         finished_correct_median_tokens=[REDACTED]print("Behavior:",behavior)z=np.load("/work/repetition_directions.npz")expected_names=[];errors=[];ratios=[]for l in range(8,23):    u=torch.from_numpy(z[str(l+1)].copy()).float()    for suffix in ["mlp.down_proj.weight","self_attn.o_proj.weight" if l%4==3 else "linear_attn.out_proj.weight"]:        name=f"model.language_model.layers.{l}.{suffix}"        expected_names.append(name);w0=load_original(name);w=load(name)        expected=w0-1.5*torch.outer(u,u@w0)        errors.append(float((w-expected).abs().max()))        ratios.append(float((w-w0).norm()/w0.norm()))        assert torch.isfinite(w).all() and w.shape==w0.shapeassert set(edited())==set(expected_names) and max(errors)==0.assert not any(n in edited() for n in ["model.language_model.norm.weight",              "model.language_model.embed_tokens.weight","lm_head.weight"])print("Audit:",dict(tensors=len(expected_names),max_reconstruction_error=max(errors),                    relative_delta_range=[min(ratios),max(ratios)]))if retain:    shutil.copytree("/work/edited","/work/final_verified_edit",dirs_exist_ok=True)else:    for name in list(edited()): drop_edit(name)manifest=dict(retained=retain,strength=1.5 if retain else 0,              tensors=list(edited()),directions="/work/repetition_directions.npz",              formula="For layer l=8..22: W'=W_original-1.5*u*u^T*W_original; u=NPZ[str(l+1)]",              results=results,behavior=behavior,              caveat="Evidence is modest: second holdout neutral; exact paired test is borderline. No benchmark results observed.")with open("/work/final_manifest.json","w") as f: json.dump(manifest,f,indent=2)with open("/work/notes.md","a") as f:    f.write("\n# FINAL DECISION: "+("RETAIN frozen strength1.5" if retain else "REVERT all edits")+"\n")    f.write("All10 forward passes used. Holdouts were preselected, disjoint, unseen, and used without tuning. Pooled results: "+json.dumps(results)+"\n")    f.write("Behavior: "+json.dumps(behavior)+"\n")    f.write("Exact final edit: 30 rank-one output-writer modifications, layers8..22 zero-based; MLP down_proj.weight and self_attn.o_proj.weight on full-attention layers11,15,19, otherwise linear_attn.out_proj.weight. W'=W_original-1.5*outer(u,u@W_original), u=/work/repetition_directions.npz[str(layer+1)]. Reconstruction audit exact; final norm/embeddings/output head and layer23 untouched. Final manifest /work/final_manifest.json; verified backup /work/final_verified_edit.\n")    f.write("Path: diagnosed repetitive thinking as dominant failure; rejected four-neuron MLP ablation and 50%/100% copying-head ablations. Derived repetition directions from matched repeated versus first-occurrence content, orthogonalized against normal reasoning, prompt, first self-checks and answer-transition controls. Strength1 and1.5 interventions reduced state projections; both had positive development paired accuracy. Strength1.5 frozen before two heldouts. Pooled gain exceeds predeclared2SE threshold narrowly; second holdout neutral and exact paired test borderline. Do not overclaim robustness or benchmark performance.\n")

# Hypothesis: the frozen candidate's pooled held-out accuracy gain meets the prespecified retention rule despite a neutral second holdout.import os,json,numpy as np,torch,hashlib,shutilfrom scipy.stats import binomtestdef read_final(path):    records=[json.loads(line) for line in open(path)]    return [dict(s,id=r["id"],source=r["source"],sample=j) for r in records for j,s in enumerate(r["samples"])]def final_stats(a,b):    assert [(s["id"],s["sample"]) for s in a]==[(s["id"],s["sample"]) for s in b]    x=np.array([s["correct"] for s in a],float)    y=np.array([s["correct"] for s in b],float)    d=y-x; gain=float(d.mean());se=float(d.std(ddof=1)/np.sqrt(len(d)))    rescued=int((d==1).sum());regressed=int((d==-1).sum())    return dict(n=len(d),original_correct=int(x.sum()),edited_correct=int(y.sum()),                original_accuracy=float(x.mean()),edited_accuracy=float(y.mean()),                gain_pp=100*gain,se_pp=100*se,                normal_95ci_pp=[100*(gain-1.96*se),100*(gain+1.96*se)],                rescued=rescued,regressed=regressed,                exact_mcnemar_p=float(binomtest(rescued,rescued+regressed,.5).pvalue),                original_cut=sum(s["cut"] for s in a),edited_cut=sum(s["cut"] for s in b),                original_mean_tokens=[REDACTED] for s in a])),                edited_mean_tokens=[REDACTED] for s in b])))oa=read_final("/work/gen_12_original.jsonl");ea=read_final("/work/gen_12_edited.jsonl")ob=read_final("/work/gen_14_original.jsonl");eb=read_final("/work/gen_14_edited.jsonl")plan=json.load(open("/work/validation_plan.json"))assert {s["id"] for s in oa}==set(plan["holdout_A"])assert {s["id"] for s in ob}==set(plan["holdout_B"])assert not({s["id"] for s in oa}&{s["id"] for s in ob})original,modified=oa+ob,ea+ebresults={"holdout_A":final_stats(oa,ea),"holdout_B":final_stats(ob,eb),         "pooled":final_stats(original,modified)}for src in ["gsm8k","math"]:    results[src]=final_stats([s for s in original if s["source"]==src],                             [s for s in modified if s["source"]==src])for k,v in results.items(): print(k,v)p=results["pooled"]retain=p["gain_pp"]>2*p["se_pp"] and all(results[s]["gain_pp"]>-2*results[s]["se_pp"] for s in ["gsm8k","math"])print("Prespecified retention threshold met:",retain,"gain/SE:",round(p["gain_pp"]/p["se_pp"],3))behavior={}for label,ss in [("original",original),("edited",modified)]:    fractions=[]    for s in ss:        ids=tok.encode(s["text"]).ids[-2048:]        grams=[tuple(ids[i:i+12]) for i in range(max(0,len(ids)-11))]        fractions.append(1-len(set(grams))/len(grams) if grams else 0.)    finished=[s["tokens"] for s in ss if s["correct"] and not s["cut"]]    behavior[label]=dict(mean_tail_repetition=float(np.mean(fractions)),                         finished_correct=len(finished),                         finished_correct_median_tokens=[REDACTED]print("Behavior:",behavior)z=np.load("/work/repetition_directions.npz")expected_names=[];errors=[];ratios=[]for l in range(8,23):    u=torch.from_numpy(z[str(l+1)].copy()).float()    for suffix in ["mlp.down_proj.weight","self_attn.o_proj.weight" if l%4==3 else "linear_attn.out_proj.weight"]:        name=f"model.language_model.layers.{l}.{suffix}"        expected_names.append(name);w0=load_original(name);w=load(name)        expected=w0-1.5*torch.outer(u,u@w0)        errors.append(float((w-expected).abs().max()))        ratios.append(float((w-w0).norm()/w0.norm()))        assert torch.isfinite(w).all() and w.shape==w0.shapeassert set(edited())==set(expected_names) and max(errors)==0.assert not any(n in edited() for n in ["model.language_model.norm.weight",              "model.language_model.embed_tokens.weight","lm_head.weight"])print("Audit:",dict(tensors=len(expected_names),max_reconstruction_error=max(errors),                    relative_delta_range=[min(ratios),max(ratios)]))if retain:    shutil.copytree("/work/edited","/work/final_verified_edit",dirs_exist_ok=True)else:    for name in list(edited()): drop_edit(name)manifest=dict(retained=retain,strength=1.5 if retain else 0,              tensors=list(edited()),directions="/work/repetition_directions.npz",              formula="For layer l=8..22: W'=W_original-1.5*u*u^T*W_original; u=NPZ[str(l+1)]",              results=results,behavior=behavior,              caveat="Evidence is modest: second holdout neutral; exact paired test is borderline. No benchmark results observed.")with open("/work/final_manifest.json","w") as f: json.dump(manifest,f,indent=2)with open("/work/notes.md","a") as f:    f.write("\n# FINAL DECISION: "+("RETAIN frozen strength1.5" if retain else "REVERT all edits")+"\n")    f.write("All10 forward passes used. Holdouts were preselected, disjoint, unseen, and used without tuning. Pooled results: "+json.dumps(results)+"\n")    f.write("Behavior: "+json.dumps(behavior)+"\n")    f.write("Exact final edit: 30 rank-one output-writer modifications, layers8..22 zero-based; MLP down_proj.weight and self_attn.o_proj.weight on full-attention layers11,15,19, otherwise linear_attn.out_proj.weight. W'=W_original-1.5*outer(u,u@W_original), u=/work/repetition_directions.npz[str(layer+1)]. Reconstruction audit exact; final norm/embeddings/output head and layer23 untouched. Final manifest /work/final_manifest.json; verified backup /work/final_verified_edit.\n")    f.write("Path: diagnosed repetitive thinking as dominant failure; rejected four-neuron MLP ablation and 50%/100% copying-head ablations. Derived repetition directions from matched repeated versus first-occurrence content, orthogonalized against normal reasoning, prompt, first self-checks and answer-transition controls. Strength1 and1.5 interventions reduced state projections; both had positive development paired accuracy. Strength1.5 frozen before two heldouts. Pooled gain exceeds predeclared2SE threshold narrowly; second holdout neutral and exact paired test borderline. Do not overclaim robustness or benchmark performance.\n")

OUTPUT of Python run 59 of at most 300:

holdout_A {'n': 256, 'original_correct': 25, 'edited_correct': 43, 'original_accuracy': 0.09765625, 'edited_accuracy': 0.16796875, 'gain_pp': 7.03125, 'se_pp': 2.435892978296806, 'normal_95ci_pp': [2.25689976253826, 11.80560023746174], 'rescued': 29, 'regressed': 11, 'exact_mcnemar_p': 0.006426576095691416, 'original_cut': 236, 'edited_cut': 210, 'original_mean_tokens': 30619.296875, 'edited_mean_tokens': 28825.8359375}holdout_B {'n': 256, 'original_correct': 37, 'edited_correct': 38, 'original_accuracy': 0.14453125, 'edited_accuracy': 0.1484375, 'gain_pp': 0.390625, 'se_pp': 2.6254111999944643, 'normal_95ci_pp': [-4.75518095198915, 5.53643095198915], 'rescued': 23, 'regressed': 22, 'exact_mcnemar_p': 1.0, 'original_cut': 219, 'edited_cut': 211, 'original_mean_tokens': 28831.27734375, 'edited_mean_tokens': 28906.03515625}pooled {'n': 512, 'original_correct': 62, 'edited_correct': 81, 'original_accuracy': 0.12109375, 'edited_accuracy': 0.158203125, 'gain_pp': 3.7109375, 'se_pp': 1.7949620696411057, 'normal_95ci_pp': [0.19281184350343275, 7.229063156496568], 'rescued': 52, 'regressed': 33, 'exact_mcnemar_p': 0.050250895335291444, 'original_cut': 455, 'edited_cut': 421, 'original_mean_tokens': 29725.287109375, 'edited_mean_tokens': 28865.935546875}gsm8k {'n': 256, 'original_correct': 41, 'edited_correct': 48, 'original_accuracy': 0.16015625, 'edited_accuracy': 0.1875, 'gain_pp': 2.734375, 'se_pp': 2.949965565341981, 'normal_95ci_pp': [-3.0475575080702826, 8.516307508070282], 'rescued': 32, 'regressed': 25, 'exact_mcnemar_p': 0.42704285650325524, 'original_cut': 221, 'edited_cut': 200, 'original_mean_tokens': 29049.421875, 'edited_mean_tokens': 27949.15625}math {'n': 256, 'original_correct': 21, 'edited_correct': 33, 'original_accuracy': 0.08203125, 'edited_accuracy': 0.12890625, 'gain_pp': 4.6875, 'se_pp': 2.050133753407993, 'normal_95ci_pp': [0.6692378433203333, 8.705762156679667], 'rescued': 20, 'regressed': 8, 'exact_mcnemar_p': 0.03569813817739487, 'original_cut': 234, 'edited_cut': 221, 'original_mean_tokens': 30401.15234375, 'edited_mean_tokens': 29782.71484375}Prespecified retention threshold met: True gain/SE: 2.067Behavior: {'original': {'mean_tail_repetition': 0.857325466628092, 'finished_correct': 56, 'finished_correct_median_tokens': 4423.0}, 'edited': {'mean_tail_repetition': 0.8245674732373471, 'finished_correct': 81, 'finished_correct_median_tokens': 9440.0}}Audit: {'tensors': 30, 'max_reconstruction_error': 0.0, 'relative_delta_range': [0.03384541720151901, 0.05989379063248634]}

holdout_A {'n': 256, 'original_correct': 25, 'edited_correct': 43, 'original_accuracy': 0.09765625, 'edited_accuracy': 0.16796875, 'gain_pp': 7.03125, 'se_pp': 2.435892978296806, 'normal_95ci_pp': [2.25689976253826, 11.80560023746174], 'rescued': 29, 'regressed': 11, 'exact_mcnemar_p': 0.006426576095691416, 'original_cut': 236, 'edited_cut': 210, 'original_mean_tokens': 30619.296875, 'edited_mean_tokens': 28825.8359375}holdout_B {'n': 256, 'original_correct': 37, 'edited_correct': 38, 'original_accuracy': 0.14453125, 'edited_accuracy': 0.1484375, 'gain_pp': 0.390625, 'se_pp': 2.6254111999944643, 'normal_95ci_pp': [-4.75518095198915, 5.53643095198915], 'rescued': 23, 'regressed': 22, 'exact_mcnemar_p': 1.0, 'original_cut': 219, 'edited_cut': 211, 'original_mean_tokens': 28831.27734375, 'edited_mean_tokens': 28906.03515625}pooled {'n': 512, 'original_correct': 62, 'edited_correct': 81, 'original_accuracy': 0.12109375, 'edited_accuracy': 0.158203125, 'gain_pp': 3.7109375, 'se_pp': 1.7949620696411057, 'normal_95ci_pp': [0.19281184350343275, 7.229063156496568], 'rescued': 52, 'regressed': 33, 'exact_mcnemar_p': 0.050250895335291444, 'original_cut': 455, 'edited_cut': 421, 'original_mean_tokens': 29725.287109375, 'edited_mean_tokens': 28865.935546875}gsm8k {'n': 256, 'original_correct': 41, 'edited_correct': 48, 'original_accuracy': 0.16015625, 'edited_accuracy': 0.1875, 'gain_pp': 2.734375, 'se_pp': 2.949965565341981, 'normal_95ci_pp': [-3.0475575080702826, 8.516307508070282], 'rescued': 32, 'regressed': 25, 'exact_mcnemar_p': 0.42704285650325524, 'original_cut': 221, 'edited_cut': 200, 'original_mean_tokens': 29049.421875, 'edited_mean_tokens': 27949.15625}math {'n': 256, 'original_correct': 21, 'edited_correct': 33, 'original_accuracy': 0.08203125, 'edited_accuracy': 0.12890625, 'gain_pp': 4.6875, 'se_pp': 2.050133753407993, 'normal_95ci_pp': [0.6692378433203333, 8.705762156679667], 'rescued': 20, 'regressed': 8, 'exact_mcnemar_p': 0.03569813817739487, 'original_cut': 234, 'edited_cut': 221, 'original_mean_tokens': 30401.15234375, 'edited_mean_tokens': 29782.71484375}Prespecified retention threshold met: True gain/SE: 2.067Behavior: {'original': {'mean_tail_repetition': 0.857325466628092, 'finished_correct': 56, 'finished_correct_median_tokens': 4423.0}, 'edited': {'mean_tail_repetition': 0.8245674732373471, 'finished_correct': 81, 'finished_correct_median_tokens': 9440.0}}Audit: {'tensors': 30, 'max_reconstruction_error': 0.0, 'relative_delta_range': [0.03384541720151901, 0.05989379063248634]}

Kept 30 rank-one edits in /work/edited: attention and MLP output projections in layers 8–22, applying (W'=W-1.5u(u^\top W)) to repetition-associated directions. Embeddings, output head, and final norm are untouched.

Path: Diagnosed repetitive thinking as the dominant failure. Rejected ineffective neuron and copying-head ablations, then identified matched-content repetition directions while protecting ordinary-reasoning and answer-transition directions. Tested two strengths and froze the candidate before validation.

512 unseen practice problems, paired evaluation:

Source	Original	Edited
GSM8K	16.0%	18.8%
MATH	8.2%	12.9%
Combined	12.1%	15.8%

Combined gain: 3.71 percentage points, paired SE 1.79. Cutoffs decreased from 88.9% to 82.2%; successful edited answers still had substantial reasoning.

Evidence is modest: the second holdout was neutral, and the pooled exact paired test was borderline. The edit narrowly met the prespecified retention rule. I expect it to help by reducing repetition, not by adding mathematical knowledge or forcing thinking to end.

Details and reconstruction instructions: /work/final_manifest.json and /work/notes.md.