Started building math proof visualizations

Summary

In Progress

Setup
Connected to traces and prepared environment.
Setup
Understand
Reviewed OpenAI math repository and proof concepts.
Understand
Plan
Planned to build 3D surfaces incorporating Blender elements.
Plan

No content

The command below was run directly in Claude Code, not sent to you as a request, and its output goes straight to the user. It's recorded here as context for later messages.

Caveat: The command below was run directly in Claude Code, not sent to you as a request, and its output goes straight to the user. It's recorded here as context for later messages.

Look at this repo: https://github.com/openai/math I'd like to build some surfaces that help people visualize and explore the concepts set out in these proofs. They should make these complex ideas accessible and interactive to people who maybe have never heard of these concepts before. Use the traces mcp to build a surface. Start by selecting one of the proofs and we can explort from there.

I'll start by grabbing the surface build instructions and looking at the repo in parallel.

{
  "type": "tool_reference",
  "tool_name": "mcp__traces__surface_build_instructions"
}
{
  "type": "tool_reference",
  "tool_name": "mcp__traces__traces_search_tools"
}
{
  "type": "tool_reference",
  "tool_name": "mcp__traces__traces_execute_tool"
}

>mkdir -p /private/tmp/claude-501/-Users-karamelton/189903e3-2f4c-44dd-8020-b4552bf40364/scratchpad/repo && cd /private/tmp/claude-501/-Users-karamelton/189903e3-2f4c-44dd-8020-b4552bf40364/scratchpad/repo && git clone --depth 1 https://github.com/openai/math 2>&1 | tail -2 && find math -type f -not -path '*/.git/*' | head -100

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math/LICENSE
math/overview.tex
math/overview.pdf
math/history.md
math/README.md
math/CONTENTS.md
math/reasoning_traces/relativistic-vlasov-maxwell.pdf
math/reasoning_traces/kaplansky-direct-finiteness-characteristic-two.pdf
math/reasoning_traces/free-group-factor-isomorphism.pdf
math/reasoning_traces/spontaneous-magnetization-quantum-heisenberg-ferromagnet.pdf
math/reasoning_traces/quasipolynomial-arithmetic-progressions.pdf
math/reasoning_traces/mezard-parisi-formula.tex
math/reasoning_traces/irrationality-exponent-of-pi.pdf
math/reasoning_traces/symmetric-and-general-mahler-conjectures.pdf
math/reasoning_traces/mezard-parisi-formula.pdf
math/reasoning_traces/ordinary-two-point-correlations.pdf
math/reasoning_traces/basic-semidefinite-threshold-np-hardness.pdf
math/lean/LICENSE
math/lean/OAI.lean
math/lean/lean-toolchain
math/lean/formalization.yaml
math/lean/README.md
math/lean/lake-manifest.json
math/lean/.gitignore
math/lean/lakefile.lean
math/lean/ComparatorChallenges/CKSBondiPenrose.json
math/lean/ComparatorChallenges/TorsionFreeZeroDivisors.lean
math/lean/ComparatorChallenges/EntangledGames.json
math/lean/ComparatorChallenges/KLInvariance.lean
math/lean/ComparatorChallenges/ParityLifts.lean
math/lean/ComparatorChallenges/SubpolynomialLp.json
math/lean/ComparatorChallenges/SnakyTwentyOne.json
math/lean/ComparatorChallenges/SLELowerPositivity.json
math/lean/ComparatorChallenges/Heisenberg.json
math/lean/ComparatorChallenges/PlanarFalconer.json
math/lean/ComparatorChallenges/SingleFold.json
math/lean/ComparatorChallenges/QuantitativeVanDerWaerden.json
math/lean/ComparatorChallenges/CartierChartCompactnessSupport.lean
math/lean/ComparatorChallenges/BoltzmannNonuniqueness.json
math/lean/ComparatorChallenges/EuclideanRamseyTransitive.lean
math/lean/ComparatorChallenges/MassAction.json
math/lean/ComparatorChallenges/KahlerSplitting.lean
math/lean/ComparatorChallenges/SnakyCertificate.json
math/lean/ComparatorChallenges/GaussianFiniteEntropy.json
math/lean/ComparatorChallenges/HarmonicArtin.json
math/lean/ComparatorChallenges/StandardMapComponents.json
math/lean/ComparatorChallenges/SurfaceImmersion.lean
math/lean/ComparatorChallenges/PrimeGaps.json
math/lean/ComparatorChallenges/SnakyConditional.lean
math/lean/ComparatorChallenges/TwoWayComplementation.json
math/lean/ComparatorChallenges/DukePrimeDegree.lean
math/lean/ComparatorChallenges/DirectCrouzeix.json
math/lean/ComparatorChallenges/SharpCAT0Filling.lean
math/lean/ComparatorChallenges/KadisonRingrose.lean
math/lean/ComparatorChallenges/AuslanderReiten.lean
math/lean/ComparatorChallenges/CriticalSKMixing.lean
math/lean/ComparatorChallenges/PettyProjectionVolume.json
math/lean/ComparatorChallenges/SATVariance.lean
math/lean/ComparatorChallenges/CliqueFreeLog.lean
math/lean/ComparatorChallenges/BoundedRecovery.json
math/lean/ComparatorChallenges/SingleLatticeCovering.json
math/lean/ComparatorChallenges/SquareDifference.json
math/lean/ComparatorChallenges/DirectedFeedback.lean
math/lean/ComparatorChallenges/HadwigerCounterexample.lean
math/lean/ComparatorChallenges/AtomicGaussian.json
math/lean/ComparatorChallenges/HarmonicGrowth.json
math/lean/ComparatorChallenges/CharacterCriterion.lean
math/lean/ComparatorChallenges/BiholderTransport.json
math/lean/ComparatorChallenges/SKRatio.lean
math/lean/ComparatorChallenges/OrdinaryElliott.json
math/lean/ComparatorChallenges/BinaryMatching.json
math/lean/ComparatorChallenges/MatrixFields.lean
math/lean/ComparatorChallenges/GrahamSpherical.lean
math/lean/ComparatorChallenges/MatchingPSD.json
math/lean/ComparatorChallenges/BinaryEditLower.json
math/lean/ComparatorChallenges/ThreeStateSupercritical.json
math/lean/ComparatorChallenges/SeymourSecondNeighborhood.json
math/lean/ComparatorChallenges/Catalan.lean
math/lean/ComparatorChallenges/StructuralCrouzeix.json
math/lean/ComparatorChallenges/VariableWL.json
math/lean/ComparatorChallenges/KMedianThreshold.json
math/lean/ComparatorChallenges/VertexCover.lean
math/lean/ComparatorChallenges/EntropyPhotonNumber.lean
math/lean/ComparatorChallenges/ExactFourier.lean
math/lean/ComparatorChallenges/CoboundaryExpanders.json
math/lean/ComparatorChallenges/BalancedBoxRouting.lean
math/lean/ComparatorChallenges/FoulkesHowe.json
math/lean/ComparatorChallenges/FinitisticAsymmetry.json
math/lean/ComparatorChallenges/CoarseAssembly.lean
math/lean/ComparatorChallenges/BalancedThreeStack.lean
math/lean/ComparatorChallenges/KoebeCircleDomains.json
math/lean/ComparatorChallenges/HotSpots.json
math/lean/ComparatorChallenges/BinPackingGap.lean
math/lean/ComparatorChallenges/MahlerConjecture.json
math/lean/ComparatorChallenges/EilenbergGanea.json
math/lean/ComparatorChallenges/Naimark.json
math/lean/ComparatorChallenges/DimensionTenChannel.lean
math/lean/ComparatorChallenges/StronglyRayleighDPP.lean
math/lean/ComparatorChallenges/TotientAsymptotic.lean
math/lean/ComparatorChallenges/BassTorsionFree.json
Shell cwd was reset to /Users/karamelton

Also, if useful... incorporate elements from blender since we have the MCP

No content

Canonical source: https://traces.com/surfaces.md
Content SHA-256: 86b2fc1482a8081bed50bfccac1049c9973eab53ae3b54dd83d2a86505d324ef
Latest trace in this namespace (use it to try each uploaded version): https://traces.com/s/8d42792e-3342-5505-8fe2-d099f8fdb7a1
Workflow: write the HTML locally, then traces_surfaces_prepare_upload -> upload -> traces_surfaces_complete_upload (release defaults to false). Share the returned previewUrl with the user; it renders that exact version on their latest trace without changing what others see. Call traces_surfaces_release_version only when the user is happy.

---
name: create-surfaces
description: Create, revise, debug, or validate Traces surfaces: single-file HTML visualizations that run in the sandboxed trace-page iframe through window.traces.getTrace(). Use when building a new surface, changing its layout or interaction, diagnosing loading/rendering, or checking an artifact before publication.
---

# Prompt: build a Traces surface

You are writing a **Traces surface**: a single self-contained HTML file that renders a view of one AI agent trace (a recorded coding-agent session: messages, tool calls, thinking, token usage).

Traces stores your file as an immutable version, serves it from its own storage, and runs it inside a locked-down iframe on the trace page. The user opens it as a tab next to "Highlights" and "Full Trace".

## Output

One HTML file. Nothing else — no build step, no bundler, no package.json.

## Hard constraints (these are enforced; violating them means it won't publish or won't run)

- **Single file, ≤ 16 MiB.** All CSS and JS inline in the document.
- **Must contain a `<head>` element.**
- **No external resources at all.** No `<link>`, `<iframe>`, `<object>`, `<embed>`, `<base>` tags. No `@import`. No `src`/`action`/`formaction`/`poster` attribute pointing at `http:`, `https:` or `//`. That means no CDN scripts, no Google Fonts, no external stylesheets, no remote images.
- **Do not make outbound network requests.** CSP is `default-src 'none'; connect-src 'none'`, and the iframe is `sandbox="allow-scripts"` without `allow-same-origin`. `fetch`, `XMLHttpRequest`, WebSockets, and form submission all fail. You have no origin, no cookies, no `localStorage`, no access to the host page.
- **Upload screening rejects recognizable WebRTC access and scripted navigation**, including `RTCPeerConnection`, writes to `location` (except `hash`), `location.assign`/`replace`, `window.open`, meta refresh, and `javascript:` URLs. Use in-page controls rather than navigation. Scripts and inline event handlers must parse as JavaScript; each script is limited to 500,000 tokens. Comments, JSON data blocks, ordinary anchors and local variables are not treated as browser API use. Screening is best-effort, not a guarantee that arbitrary code cannot send data out.
- **Allowed:** inline `<script>` and inline `<style>`, `data:` URIs for images and fonts, inline SVG, canvas, and everything else that runs purely in-page.
- **Vendoring a library is allowed** but it must be inlined in the file and it counts against the 16 MiB. Prefer writing it yourself — a big bundle costs parse/compile time on every open, and the iframe shares nothing with the host page.

## The SDK

The host injects a global before your code runs:

```js
window.traces.version    // "surface-sdk.v1"
await window.traces.getTrace()   // Promise<SurfaceTrace>
```

`getTrace()` is the entire data API. There is exactly one call, it resolves once with everything you get, and there is no way to ask for more data. It rejects if the host doesn't deliver within 10 seconds — handle that and render a readable message rather than a blank page.

Data is pushed to you over a private channel; you never request it.

### Classifying (beta)

Classification is a beta feature and needs a signed-in viewer. For anyone signed out it fails with `errorCode: "unauthenticated"`, so every surface that uses it needs a fallback.

`window.traces.classify` answers typed questions about any JSON state you build from the trace:

```js
const result = await window.traces.classify({
  state: { command: call.args.command },
  questions: {
    destructive: { type: "bool", instructions: "Does `command` delete data?",
                   criteria: { true: "Removes files or history", false: "Leaves data intact" } },
    area: { type: "choice", instructions: "What does `command` touch?",
            criteria: { git: "Version control", files: "The filesystem", network: "Remote hosts" } },
    risk: { type: "score", instructions: "How consequential is `command`?",
            criteria: ["Read-only", "Local change", "Leaves the machine"] }
  }
});
// result.answers.destructive.probability · .area.choice / .probabilities · .risk.score (0 = first level)
```

It never rejects. Check `result.stopReason === "stop"`; otherwise `result.errorMessage` says why, and `result.errorCode` says what to do:
- `"unauthenticated"`, `"forbidden"` or `"quota_exceeded"`: this viewer can't classify. Stop and say so.
- `"rate_limited"`: the viewer is calling too fast. Back off briefly and retry.
- no code: this call failed. Render the fallback; the platform has already retried what a retry can fix.

Getting good answers:
- **Decide what each answer is about**, such as a tool call, a user turn or the whole trace. The `state` is that item plus the context needed to judge it. Every question sees all of the state, so unrelated content can sway an answer, and missing context makes it guess.
- **Ask everything about an item in one call.** Questions are answered independently against the state, so they don't affect each other, and the state is paid for once. Use a separate call only when a question needs different evidence or depends on another answer.
- **Point questions at the state.** Name fields with backticked paths, such as `` `action.output` ``, rather than "this" or "the output".
- **Choose how many items share a call.** One item per call gives each answer the cleanest evidence but makes the most calls, which is slower on long traces and repeats a little overhead per item. Several items per call is faster and lighter on limits, but every question then sees the other items, and content unrelated to a question can cost accuracy. Group items when speed matters more than the last bit of accuracy, when items are short, or when the question compares them. Give single-item checks that must be right (did it fail, did it touch secrets) their own call. When you group, give each item its own field and name it in its questions.
- **Send calls in parallel.** You don't need your own retries or concurrency tuning; the platform retries the model's own failures. Handle `"rate_limited"`, and classify what's on screen first if the trace is long. Oversized requests are refused, so trim `state` to the relevant excerpt.

## The data contract

```ts
type SurfaceTrace = {
  trace: {
    id: string;
    title?: string;
    agentId: string;                       // which coding agent produced it
    namespace: { id: string; slug: string };
    author?: { id: string; displayName?: string; avatarUrl?: string };
    model?: string;
    createdAt: number;                     // epoch ms
    startedAt?: number;
    messageCount?: number;                 // total in the trace, may exceed messages.length
    tokenUsage?: TokenUsage;
    project?: { name?: string; path?: string; gitRemoteUrl?: string; gitBranch?: string };
    analysis?: TraceAiAnalysis;            // optional AI summary, may be absent
  };
  messages: SurfaceTraceMessage[];
  truncated: boolean;
};

type SurfaceTraceMessage = {
  id: string;
  role: string;                            // e.g. user / assistant / system — do not assume a closed set
  text?: string;                           // flattened text, convenient but lossy
  model?: string;
  order?: number;
  timestamp?: number;                      // epoch ms
  parts: SurfaceTracePart[];
  tokenUsage?: TokenUsage;
};

type SurfaceTracePart = {
  type: "text" | "thinking" | "tool_call" | "tool_result" | "error" | "system_event";
  order: number;
  content: Record<string, unknown>;        // shape depends on type
  tokenUsage?: TokenUsage;
};
```

Part content shapes:

- `text` / `thinking` → `{ text }`
- `tool_call` → `{ callId, toolName, args }`
- `tool_result` → `{ callId, toolName, output, status: "success" | "error" }`
- `error` → `{ message }`
- `system_event` → `{ subtype, ... }` (open-ended; extra fields ride along verbatim)

Any content may also carry `startedAt`, `completedAt`, `durationMs`. Do **not** sum `durationMs` across parts — some agents report per-part timing and others stamp a whole turn's wall-clock onto every part in it, so summing double-counts.

Pair `tool_call` with `tool_result` on `callId`, not on adjacency.

## Rules for the code you write

- **Every field except `trace.id`, `trace.agentId`, `trace.namespace`, `trace.createdAt`, `messages[].id`, `messages[].role`, `messages[].parts` and `parts[].type/order/content` is optional.** Real traces are missing things — no title, no model, no token usage, no author. Render a sensible placeholder instead of `undefined` or `NaN`, and never crash on a missing field.
- **Handle `truncated: true` honestly.** You may be given a prefix of a long trace. If `truncated` is true, or `messages.length < trace.messageCount`, say so in the UI — "over the first N of M messages" — rather than presenting a partial aggregate as if it covered the whole trace.
- **Tolerate unknown values.** New part types, new `system_event` subtypes and new roles will appear. Skip or generically render what you don't recognise; never throw.
- **Escape all trace content.** Message text and tool output are untrusted arbitrary strings, frequently containing HTML, code and markup. Use `textContent`, never `innerHTML`, for anything derived from the data.
- **Don't sort by array position.** Use `order` / `timestamp` when you need sequence, and cope with them being absent.
- **Assume it can be big.** Up to a few thousand messages. Don't render thousands of DOM nodes eagerly if you can summarise, virtualise or collapse.
- **Render something immediately**, then fill in when `getTrace()` resolves: a loading state, an error state for the rejection case, and an empty state for a trace with no messages.
- **Keep states mutually exclusive.** If you use the HTML `hidden` attribute, include `[hidden] { display: none !important; }`. Author CSS such as `.state { display: grid }` can otherwise override the browser default and expose loading, error, empty, and content states simultaneously.
- **Define the surface's semantic units from the current request.** Do not assume what a “turn,” step, token, duration, category, or aggregate means based on another surface. Label estimated or derived values as such.
- Look reasonable on both a narrow and a wide viewport; the iframe is the width of the trace page.
- Keep it self-explanatory. No settings, no persistence — there is nowhere to save anything.

## Aesthetics — match traces.com

The surface renders inside the Traces trace page, so it should look like it belongs there rather than like an embedded third-party widget. Traces is a restrained, near-monochrome, information-dense UI: flat surfaces, hairline borders, one accent colour used sparingly, no drop shadows, no gradients, no rounded-pill decoration.

**The surface is a section of the trace page, not a page of its own.** It renders in a tab directly under the trace's own `<h1>` (the trace title) and the tab bar, so it must not restate that chrome:

- **Never paint a background on the whole surface.** Leave `html` and `body` transparent — no `background: var(--background)` on the root. `--background` is still fine for a *recessed* fill inside a card, just never on the root.
- **Never open with a title, an `<h1>`, or eyebrow text, and don't add a heading just because it's a section.** It's part of a layout, so start with the content — the tab name already acts as the surface's header. Add an `<h2>` only where it earns its place: a later section the tab name doesn't cover, or a sibling that needs telling apart.
- **Never add outer padding or margin on `html`/`body`.** The frontend already applies the page's outer gutter around the iframe; a root padding doubles it up as dead space. Put spacing on your own inner elements instead — `body { margin: 0 }`, no `padding` on `html` or `body`.

```html
<!-- Don't: a page header the trace page already gives you -->
<body>
  <p class="eyebrow">SKILL ACTIVITY</p>
  <h1>Skills loaded in this run</h1>
  <p class="lede">Open a skill to read its instructions and see every recorded load.</p>
  <section class="stats">…</section>
</body>

<!-- Do: open on the content; section headings from h2 down -->
<body>
  <section class="stats">…</section>
  <h2>Loaded skills</h2>
  <ul class="skill-list">…</ul>
</body>
```

**Always style both light and dark — never ship just one.** The host gives you no theme signal. Read `@media (prefers-color-scheme: dark)` and define both, for every color you introduce, not just the tokens below. Declare `color-scheme: light dark` so form controls and scrollbars follow.

Copy these tokens verbatim — they're the real values from the app:

```css
:root {
  color-scheme: light dark;
  --background: #f5f5f5;
  --foreground: #141414;
  --card: #ffffff;
  --muted: #f2f2f2;             /* recessed fills */
  --muted-foreground: #6b6b6b;  /* labels, secondary text */
  --faint-foreground: #808080;  /* timestamps, least important text */
  --border: #e3e3e3;
  --border-strong: #dbdbdb;
  --hairline: rgb(0 0 0 / 0.07);
  --accent: rgb(0 0 0 / 0.03);  /* hover fill */
  --primary: oklch(0.50 0.15 278);   /* indigo-violet; links, selection, focus */
  --destructive: #cf4415;       /* errors, deletions */
  --success: #4b7c0b;           /* success, additions */
  --radius: 0.625rem;           /* 10px cards; 5px for small controls */
}

@media (prefers-color-scheme: dark) {
  :root {
    --background: #141414;
    --foreground: #e2e2e2;
    --card: #262626;
    --muted: #111111;
    --muted-foreground: #a0a0a0;
    --faint-foreground: #737373;
    --border: #202020;
    --border-strong: #242424;
    --hairline: rgb(226 226 226 / 0.06);
    --accent: rgb(255 255 255 / 0.03);
  }
}
```

**Type.** Body is Inter in the app; you can't load it, so use `font-family: Inter, ui-sans-serif, system-ui, sans-serif` and let it fall back. Code, tool names, file paths, hashes, IDs and numeric columns are monospace: `ui-monospace, "Berkeley Mono", SFMono-Regular, Menlo, monospace`, ~13px. Body text 13–14px, section labels ~12px in `--muted-foreground`, occasionally uppercase with slight letter-spacing. **Inter is the only typeface** — no display, serif, or decorative face for titles or emphasis — unless the user's request explicitly names another one. Monospace, as above, is the only other family, reserved for code/numeric content.

**Weight.** Only two font weights: `font-weight: 400` (normal) and `font-weight: 500` (medium). Never 600/700/bold/semibold. Titles — including `<h2>`, the largest text you have — are `font-weight: 400`; size and tight tracking (`letter-spacing: -0.01em` to `-0.02em`) carry the hierarchy, not weight. Reserve `500` for small text that needs to read as distinct from adjacent body text at that *same* size — a list-item's title next to its description, a label next to its value. Everything else is 400.

**Restraint.** Use as few distinct type styles and spacing values as possible (ex: one font size, one text colour, one weight, one spacing unit — vary just one property when a genuinely different element needs a distinction).

**Layout.** Use `--card` sparingly: `1px solid --border`, `border-radius: var(--radius)`, no shadow. Prefer a single hairline (`--hairline`) between list rows over boxing each one. Hover = `--accent` fill, not a colour change. Keep the palette monochrome; `--primary` for links/focus/one emphasis, `--destructive`/`--success` only for genuine error/success.

**Density.** Traces is comfortable with dense tables and tight rows — prefer a compact table or list over big cards with lots of padding. Right-align and monospace numbers so columns line up. Format timestamps as short absolute dates (or relative for recent), and format bytes/tokens with thousands separators rather than raw integers.

**Sizing.** The frame is the full width of the trace page and **grows to fit your content** — the SDK measures your layout and the host resizes the iframe, so the trace page does the scrolling. Lay out naturally (`body { margin: 0 }`, no fixed-height wrapper) and do not build your own inner scroll container: a `height: 100vh` root or an internally scrolling shell gets you a nested scrollbar, and the host deliberately refuses to grow past the viewport for surfaces whose height tracks the viewport. Minimum height is 32rem, maximum 1000000px. Don't add your own outer border or page background that would double up the frame's. **Don't cap the surface with a `max-width` and center it — use the full width you're given.** A centered column reads as a floating card rather than the tab's own content.

**Motion.** Almost none. If you animate, keep it under ~150ms and wrap it in `@media (prefers-reduced-motion: reduce)` so it can be turned off.

Do not publish, install, set a current version, commit, or change the API/frontend runtime unless the user explicitly asks.

## Trying it on a real trace

A surface can only be seen on a trace, so the loop is: upload a version, try it, then release it. Uploading does not change what anyone else sees — a version is invisible until it is made current.

1. Prepare an upload for the surface key and a new semver (`traces_surfaces_prepare_upload` via the Traces MCP, or `POST /v1/surfaces/:key/versions/uploads` with an API key). You get back a short-lived, single-use `upload` destination.
2. Send the raw HTML file to it directly, e.g. `curl -fsS -X POST -H 'Content-Type: text/html' --data-binary @surface.html <url>`. The response contains `artifactId`. Never paste the HTML into a tool call.
3. Complete the upload (`traces_surfaces_complete_upload`, or `POST …/uploads/complete`) with that `artifactId`. Leave `release` off.
4. Open the version on a trace: `https://traces.com/s/<trace-id>?surface=<key>&version=<version>`. The MCP tool returns this link for the namespace's latest trace as `previewUrl`; share it with the user. Only namespace members can open a version that is not current.
5. Fix, bump the version, repeat. When the user is happy, release it (`traces_surfaces_release_version`, or `PATCH /v1/surfaces/:key`) to make it current.

## Now build

Use the user's requested surface description to decide what the surface should show and how. If that description is missing or leaves a consequential semantic choice unresolved, ask a focused question before building.

>cd /private/tmp/claude-501/-Users-karamelton/189903e3-2f4c-44dd-8020-b4552bf40364/scratchpad/repo/math && cat README.md | head -80 && echo ---- && head -150 CONTENTS.md && ls

# Readme

This repository contains mathematical manuscripts and supporting proof artifacts produced by an internal OpenAI model. 

As part of model development, we evaluate our models on open research problems. We expanded these evaluations after performance on our existing mathematical evaluations saturated. Some outputs build upon earlier results produced by the models.

This collection includes results at different stages of verification. Not all have accompanying Lean formalizations. We will continue to update this repository with Lean formalizations as we obtain them. 

Some of the unformalized results could have issues. We will endeavor to fix any such issues quickly.
We are also exploring community-hosted repositories for these materials. 

## Navigating the collection

The current catalogue contains 719 manuscripts organized into 372 families. A family groups related papers, which may include a principal result, companion arguments, consequences, or alternative proofs. Each family is classified by mathematical discipline.

- Start with the [overview](overview.pdf) for descriptions of the families.
- Use the [manuscript map](CONTENTS.md) to find individual papers and their supporting materials.
- The [`preprints/`](preprints/) directory contains PDFs, source files, and manuscript-specific citation and build instructions.
- The [Lean library](lean/README.md) and [formalization catalogue](lean/formalization.yaml) describe the available formal proofs, their associated papers, and verification configurations. See the [Comparator instructions](lean/ComparatorChallenges/README.md) for additional checking instructions. The repository has ~42% top-line results formalized.
- Updates to the repo are described in the [history](history.md).

### Reasoning summaries

We are also releasing abridged summaries of the model's reasoning, covering the following results:

| Family | Subject |
|---|---|
| 007 | [Ordinary two-point correlations of multiplicative functions](reasoning_traces/ordinary-two-point-correlations.pdf) |
| 017 | [The irrationality exponent of π](reasoning_traces/irrationality-exponent-of-pi.pdf) |
| 087 | [Symmetric and general Mahler conjectures](reasoning_traces/symmetric-and-general-mahler-conjectures.pdf) |
| 102 | [Ordinary NP-hardness at the basic semidefinite threshold](reasoning_traces/basic-semidefinite-threshold-np-hardness.pdf) |
| 159 | [Quasipolynomial bounds for arithmetic progressions](reasoning_traces/quasipolynomial-arithmetic-progressions.pdf) |
| 197 | [Kaplansky's direct-finiteness conjecture in characteristic two](reasoning_traces/kaplansky-direct-finiteness-characteristic-two.pdf) |
| 221 | [The Mézard–Parisi formula for diluted spin glasses](reasoning_traces/mezard-parisi-formula.pdf) |
| 271 | [Spontaneous magnetization in the quantum Heisenberg ferromagnet](reasoning_traces/spontaneous-magnetization-quantum-heisenberg-ferromagnet.pdf) |
| 287 | [Isomorphism of free group factors](reasoning_traces/free-group-factor-isomorphism.pdf) |
| 362 | [The three-dimensional relativistic Vlasov–Maxwell system](reasoning_traces/relativistic-vlasov-maxwell.pdf) |


## How the results were produced

The vast majority of results were obtained with the same procedure using an unreleased internal OpenAI model. On average, each result used three hours of ChatGPT Pro thinking compute with that model. Over the course of the evaluation, the model was posed approximately 4,000 problems. Aggregating the output into result families and manuscripts and requiring an appropriate level of significance led to the catalog outlined above.

Exceptions to this fixed procedure include work on a zero-free region for the Riemann zeta function and proof of the Hodge Conjecture for CM abelian varieties. Additionally, the writeup for the Re(s) > 11/12 zero-free region for the Riemann zeta function was human edited for readability. 


## Versions and citations

We will preserve the public release history of this collection. Corrections and revisions will be recorded as new versions, with previously released versions remaining accessible. 

To cite the individual manuscript, use the BibTeX block in its directory. 
----
# Mathematics manuscript collection

**719 manuscripts covering 372 result families.**

[**Read the overview PDF**](overview.pdf).

## Manuscript map

Each result description is followed by its constituent manuscripts and their abstracts. Paper titles link directly to PDFs.

<table>
<thead><tr><th>Result</th></tr></thead>
<tbody><tr>
<td>

**001. Milne’s rationality conjecture and algebraic specialization.** Proves Milne's rationality conjecture for abelian varieties over $`\overline{\mathbb Q}`$ with good reduction: specialized Hodge classes pair rationally with complementary divisor products, independently of cohomology theory. Together with result 032, every specialized Hodge class is represented by a single rational algebraic cycle simultaneously in all prime-to-<i>p</i> and crystalline realizations, for every residue characteristic <i>p</i>.

</td>
</tr></tbody>
<tbody><tr>
<td>

&emsp;[Milne's rationality conjecture for abelian varieties](preprints/Milnes-rationality-conjecture-for-abelian-varieties-October-7-2026/paper.pdf)

We prove Milne's rationality conjecture for abelian varieties, including residue characteristic 2. After good reduction, the pairing of a rational Hodge class with any complementary product of divisor classes on the reduction is the same rational number in every prime-to-<i>p</i> realization and in crystalline cohomology. Using the Hodge theorem for CM abelian varieties, we also show that every such specialized Hodge class is represented by a single rational algebraic cycle in all these realizations.

</td>
</tr></tbody>
<tbody><tr>
<td>

**002. The full BSD formula from low Selmer corank.** Proves the full Birch–Swinnerton-Dyer leading-term formula for every elliptic curve over ℚ whose full <i>q</i>-power Selmer group has corank zero or one for some prime <i>q</i>, including finiteness of the Tate–Shafarevich group. With result 006, this gives full BSD for a density-one set of quadratic twists of every elliptic curve over ℚ.

</td>
</tr></tbody>
<tbody><tr>
<td>

&emsp;[Exact Birch–Swinnerton-Dyer Formula from Low Selmer Corank](preprints/Exact-Birch-Swinnerton-Dyer-Formula-from-Low-Selmer-Corank-October-7-2026/exact-bsd-low-selmer-corank.pdf)

We prove the full Birch–Swinnerton-Dyer leading-term formula for every elliptic curve over ℚ whose full <i>q</i>-power Selmer group has corank zero or one at some prime <i>q</i>. The analytic and Mordell–Weil ranks equal that corank, and the Tate–Shafarevich group is finite. The formula includes all prime factors and requires no additional hypotheses on reduction, rational torsion, isogenies, complex multiplication, or residual Galois representations.

</td>
</tr></tbody>
<tbody><tr>
<td>

&emsp;[The Selmer converse for elliptic curves at every prime](preprints/The-Selmer-converse-for-elliptic-curves-at-every-prime-October-7-2026/main.pdf)

We prove the Selmer converse in coranks zero and one for every elliptic curve over ℚ and every prime <i>p</i>: if the full <i>p</i>-power Selmer group has ℤ<sub><i>p</i></sub>-corank $`r\in\{0,1\}`$, then the analytic and Mordell–Weil ranks both equal <i>r</i>, and the entire Tate–Shafarevich group is finite. As an application at the additive prime 3, we prove that for every prime $`\ell\equiv4,7,8\pmod9`$, the cubic $`X^3+Y^3=\ell Z^3`$ has analytic and Mordell–Weil rank one and finite Tate–Shafarevich group. In particular, every such <i>ℓ</i> is a sum of two rational cubes.

</td>
</tr></tbody>
<tbody><tr>
<td>

&emsp;[The two-primary Birch–Swinnerton-Dyer formula in Selmer corank at most one](preprints/The-two-primary-Birch-Swinnerton-Dyer-formula-in-Selmer-corank-at-most-one-October-6-2026/paper.pdf)

We prove the two-primary Birch and Swinnerton-Dyer leading-term formula for every elliptic curve over the rationals whose two-power Selmer group has corank at most one. In this range, the algebraic rank, analytic rank, and Selmer corank are equal, and the Tate–Shafarevich group is finite. Combined with the quadratic-twist Selmer distribution, this gives the exact two-primary formula for a density-one set of signed squarefree twists of each fixed curve, ordered by absolute value; the common rank is zero or one, with each value having density one half.

</td>
</tr></tbody>
<tbody><tr>
<td>

**003. The quasi-Riemann hypothesis.** Proves that every Dirichlet <i>L</i>-function, including $`\zeta(s)`$, is zero-free in $`\Re s\gt 7/8`$, resolving the quasi-Riemann hypothesis. The same half-plane is zero-free for every finite-order Hecke <i>L</i>-function over $`\mathbb Q(\sqrt{-3})`$. A companion gives a different proof of the zero-free half-plane $`\Re s\gt 11/12`$. ([Lean](lean/docs/003.md))

</td>
</tr></tbody>
<tbody><tr>
<td>

&emsp;[The Quasi-Riemann Hypothesis: A Zero-Free Half-Plane $`\Re s\gt 7/8`$ ](preprints/The-Quasi-Riemann-Hypothesis-September-30-2026/paper.pdf)

We prove that all finite-order Hecke <i>L</i>-functions over $`\mathbb Q(\sqrt{-3})`$ and all Dirichlet <i>L</i>-functions are zero-free in the half-plane $`\Re s\gt 7/8`$, with the principal pole at <i>s</i> = 1 allowed. In particular, the Riemann zeta function is zero-free in this half-plane, proving the quasi-Riemann hypothesis.

</td>
</tr></tbody>
<tbody><tr>
<td>

&emsp;[The Quasi-Riemann Hypothesis (alternate 11/12 proof)](preprints/The-Quasi-Riemann-Hypothesis-October-5-2026/paper2.pdf)

We establish the quasi-Riemann hypothesis by proving that every Dirichlet <i>L</i>-function, including Riemann's zeta function, has no zeros in the half-plane $`\mathop{\mathrm{Re}}\nolimits s\gt 11/12`$. More generally, we prove the same zero-free half-plane for every finite-order Hecke <i>L</i>-function over $`K=\mathbb Q(\sqrt{-3})`$. In particular, this rules out the existence of Landau–Siegel zeros.

</td>
</tr></tbody>
<tbody><tr>
<td>

&emsp;[Uniform exclusion of Landau–Siegel zeros](preprints/Uniform-exclusion-of-Landau-Siegel-zeros-October-1-2026/paper.pdf)

We prove the uniform exclusion of Landau–Siegel zeros. There is an absolute constant <i>c</i> &gt; 0 such that every real zero $`\beta\in(0,1)`$ of every primitive nonprincipal real Dirichlet <i>L</i>-function of conductor <i>q</i> ≥ 3 satisfies $`(1-\beta)\log q\ge c`$.

</td>
</tr></tbody>
<tbody><tr>
<td>

**004. Hilbert’s tenth problem over ℚ.** Proves that no algorithm decides whether an integer-coefficient polynomial in an arbitrary number of variables has a rational zero, resolving Hilbert's tenth problem over ℚ negatively.

</td>
</tr></tbody>
<tbody><tr>
<td>

&emsp;[Hilbert’s tenth problem over the rational numbers](preprints/Hilberts-tenth-problem-over-the-rational-numbers-October-6-2026/main.pdf)

We give a negative answer to Hilbert's tenth problem over the rational numbers: no algorithm decides whether a polynomial with integer coefficients has a rational zero. The number of variables is part of the input.

</td>
</tr></tbody>
<tbody><tr>
<td>

&emsp;[A pointwise 2-converse for elliptic curves with rational two-torsion](preprints/A-pointwise-2-converse-for-elliptic-curves-with-rational-two-torsion-October-7-2026/paper.pdf)

We prove a pointwise 2-converse for elliptic curves over $`\mathbf Q`$ with nonzero rational two-torsion: if the $`2^\infty`$-Selmer corank is zero or one, then the analytic rank and Mordell–Weil rank equal that corank, and the Shafarevich–Tate group is finite. The result allows arbitrary reduction at 2.

</td>
</tr></tbody>
<tbody><tr>
<td>

**005. Irrationality of Catalan’s constant.** Proves that Catalan's constant $`G=\sum_{j\ge0}(-1)^j/(2j+1)^2`$ is irrational. ([Lean](lean/docs/005.md))

</td>
</tr></tbody>
<tbody><tr>
<td>

&emsp;[Catalan's constant is irrational](preprints/Catalans-constant-is-irrational-September-24-2026/paper.pdf)

We prove that Catalan's constant $`G=\sum_{j\geq0}(-1)^j/(2j+1)^2`$ is irrational.

</td>
</tr></tbody>
<tbody><tr>
<td>

**006. Goldfeld’s conjecture: densities and mean analytic rank.** Proves Goldfeld's conjecture for quadratic twists of every elliptic curve over ℚ: analytic ranks zero and one each have density 1/2, and the mean analytic rank tends to 1/2. Both statements order signed squarefree twist parameters by absolute value.

</td>
</tr></tbody>
<tbody><tr>
<td>

&emsp;[Goldfeld's analytic density conjecture and the 2-converse for elliptic curves](preprints/Goldfelds-analytic-density-conjecture-and-the-2-converse-for-elliptic-curves-October-7-2026/paper.pdf)

We prove Goldfeld's analytic density conjecture: for every elliptic curve <i>E</i> over ℚ, the quadratic twists of <i>E</i> with analytic rank zero and one each have density 1/2 among signed squarefree twist parameters ordered by absolute value. We also prove the low-corank 2-converse: if the $`2^\infty`$-Selmer corank of <i>E</i> is zero or one, then it equals the analytic and Mordell–Weil ranks, and the Tate–Shafarevich group is finite.
CONTENTS.md
history.md
lean
LICENSE
overview.pdf
overview.tex
preprints
README.md
reasoning_traces
Shell cwd was reset to /Users/karamelton

>cd /private/tmp/claude-501/-Users-karamelton/189903e3-2f4c-44dd-8020-b4552bf40364/scratchpad/repo/math && grep -n -A12 '\*\*087\.' CONTENTS.md; which pdftotext; ls reasoning_traces

2127:**087. The Mahler conjectures, functional inequalities and polar-product symplectic width.** Resolves the symmetric and nonsymmetric geometric Mahler conjectures in every dimension, with Hanner polytopes and simplices as the respective volume-product minimizers and all equality cases classified. The corresponding sharp functional Mahler inequalities also hold. For <i>n</i> ≥ 2, every symmetric polar product $`K\times K^\circ`$ in dimension $`2n`$ has Gromov width 4. ([Lean](lean/docs/087.md))
2128-
2129-</td>
2130-</tr></tbody>
2131-<tbody><tr>
2132-<td>
2133-
2134-&emsp;[The symmetric Mahler conjecture and its equality cases](preprints/The-symmetric-Mahler-conjecture-and-its-equality-cases-September-22-2026/paper.pdf)
2135-
2136-We resolve the symmetric Mahler conjecture positively, including its equality classification. Every origin-symmetric convex body in ℝ<sup><i>n</i></sup> has volume product at least $`4^n/n!`$, with equality exactly for invertible linear images of Hanner polytopes.
2137-
2138-</td>
2139-</tr></tbody>
pdftotext not found
basic-semidefinite-threshold-np-hardness.pdf
free-group-factor-isomorphism.pdf
irrationality-exponent-of-pi.pdf
kaplansky-direct-finiteness-characteristic-two.pdf
mezard-parisi-formula.pdf
mezard-parisi-formula.tex
ordinary-two-point-correlations.pdf
quasipolynomial-arithmetic-progressions.pdf
relativistic-vlasov-maxwell.pdf
spontaneous-magnetization-quantum-heisenberg-ferromagnet.pdf
symmetric-and-general-mahler-conjectures.pdf
Shell cwd was reset to /Users/karamelton

>cd /private/tmp/claude-501/-Users-karamelton/189903e3-2f4c-44dd-8020-b4552bf40364/scratchpad/repo/math && sed -n 2139,2175p CONTENTS.md

</tr></tbody>
<tbody><tr>
<td>

&emsp;[The Mahler Conjecture for General Convex Bodies](preprints/The-Mahler-Conjecture-for-General-Convex-Bodies-September-22-2026/paper.pdf)

We resolve the Mahler conjecture for general convex bodies positively. For every convex body $`K\subset\mathbb R^n`$, <i>n</i> ≥ 1, with Santaló point $`s(K)`$, $`|K|\,|(K-s(K))^\circ|\ge (n+1)^{n+1}/(n!)^2`$, with equality exactly for simplices.

</td>
</tr></tbody>
<tbody><tr>
<td>

&emsp;[Symplectic Balls in Symmetric Polar Products](preprints/Symplectic-Balls-in-Symmetric-Polar-Products-September-22-2026/paper.pdf)

For every integer <i>n</i> ≥ 2 and every origin-symmetric convex body $`K\subset\mathbb R^n`$, we prove that the Gromov width of $`\mathop{\mathrm{int}}\nolimits K\times\mathop{\mathrm{int}}\nolimits K^\circ`$ is 4. We construct smooth symplectic embeddings of standard balls of every capacity $`0\lt c\lt 4`$ into this polar product. Volume preservation then resolves the symmetric Mahler conjecture positively in every dimension.

</td>
</tr></tbody>
<tbody><tr>
<td>

**088. Sharp projection-body inequalities and a counterexample to simplex maximization.** Proves Petty's projection-volume conjecture in the remaining dimensions <i>n</i> ≥ 4: ellipsoids uniquely minimize projection-body volume at fixed body volume. Also establishes the full Lutwak–Petty projection inequalities. In contrast, products of simplices exceed Brannen's proposed simplex maximum for normalized projection-body volume by an exponential factor in every sufficiently large dimension. ([Lean](lean/docs/088.md))

</td>
</tr></tbody>
<tbody><tr>
<td>

&emsp;[Petty’s projection-volume conjecture in dimensions at least four](preprints/Pettys-projection-volume-conjecture-in-dimensions-at-least-four-September-24-2026/paper.pdf)

We prove that ellipsoids uniquely minimize the volume of the projection body among convex bodies of fixed volume in every dimension at least four. This proves Petty's projection-volume conjecture in these dimensions.

</td>
</tr></tbody>
<tbody><tr>
<td>
Shell cwd was reset to /Users/karamelton

>cd /private/tmp/claude-501/-Users-karamelton/189903e3-2f4c-44dd-8020-b4552bf40364/scratchpad && python3 -c "import pypdf" 2>&1; python3 -c "import fitz" 2>&1; ls repo/math/preprints | grep -i mahler; ls repo/math/preprints/The-symmetric-Mahler*; ls repo/math/preprints/Symplectic-Balls*

Traceback (most recent call last):
  File "<string>", line 1, in <module>
ModuleNotFoundError: No module named 'fitz'
The-Mahler-Conjecture-for-General-Convex-Bodies-September-22-2026
The-symmetric-Mahler-conjecture-and-its-equality-cases-September-22-2026
build
paper.pdf
README.md
build
paper.pdf
README.md
Shell cwd was reset to /Users/karamelton

pypdf is available. I'll extract the reasoning summary and the paper intros.

>cd /private/tmp/claude-501/-Users-karamelton/189903e3-2f4c-44dd-8020-b4552bf40364/scratchpad && M=repo/math && python3 -I -c "
import pypdf,sys
for p,n in [(sys.argv[1],None),(sys.argv[2],3),(sys.argv[3],3)]:
  r=pypdf.PdfReader(p); print('=====',p,len(r.pages))
  t=''.join(pg.extract_text() for pg in r.pages[:n] if True)
  open('txt_'+p.split('/')[-2 if 'paper' in p else -1]+'.txt','w').write(t)
" $M/reasoning_traces/symmetric-and-general-mahler-conjectures.pdf $M/preprints/The-symmetric-Mahler-conjecture-and-its-equality-cases-September-22-2026/paper.pdf $M/preprints/Symplectic-Balls-in-Symmetric-Polar-Products-September-22-2026/paper.pdf; wc -c txt_*

Exit code 1
Traceback (most recent call last):
  File "<string>", line 2, in <module>
ModuleNotFoundError: No module named 'pypdf'
(eval):7: no matches found: txt_*

>cd /private/tmp/claude-501/-Users-karamelton/189903e3-2f4c-44dd-8020-b4552bf40364/scratchpad && M=repo/math && python3 -P -E -c "
import pypdf,sys
for p,n,o in [(sys.argv[1],None,'trace'),(sys.argv[2],4,'sym'),(sys.argv[3],4,'sympl')]:
  r=pypdf.PdfReader(p); print(o,len(r.pages))
  open('txt_'+o+'.txt','w').write('\n'.join(pg.extract_text() for pg in r.pages[:n]))
" $M/reasoning_traces/symmetric-and-general-mahler-conjectures.pdf $M/preprints/The-symmetric-Mahler-conjecture-and-its-equality-cases-September-22-2026/paper.pdf $M/preprints/Symplectic-Balls-in-Symmetric-Polar-Products-September-22-2026/paper.pdf; wc -c txt_*

Exit code 1
Unknown option: -P
usage: /Library/Developer/CommandLineTools/usr/bin/python3 [option] ... [-c cmd | -m mod | file | -] [arg] ...
Try `python -h' for more information.
(eval):6: no matches found: txt_*

>cd /private/tmp/claude-501/-Users-karamelton/189903e3-2f4c-44dd-8020-b4552bf40364/scratchpad && M=repo/math && python3 -E -c "
import pypdf,sys
for p,n,o in [(sys.argv[1],None,'trace'),(sys.argv[2],4,'sym'),(sys.argv[3],4,'sympl')]:
  r=pypdf.PdfReader(p); print(o,len(r.pages))
  open('txt_'+o+'.txt','w').write('\n'.join(pg.extract_text() for pg in r.pages[:n]))
" $M/reasoning_traces/symmetric-and-general-mahler-conjectures.pdf $M/preprints/The-symmetric-Mahler-conjecture-and-its-equality-cases-September-22-2026/paper.pdf $M/preprints/Symplectic-Balls-in-Symmetric-Polar-Products-September-22-2026/paper.pdf; wc -c txt_*

trace 45
sym 26
sympl 19
   11361 txt_sym.txt
   11095 txt_sympl.txt
  157323 txt_trace.txt
  179779 total
Shell cwd was reset to /Users/karamelton

>cd /private/tmp/claude-501/-Users-karamelton/189903e3-2f4c-44dd-8020-b4552bf40364/scratchpad && head -c 9000 txt_sym.txt; echo; echo =======; head -c 5000 txt_sympl.txt; echo ======; head -c 12000 txt_trace.txt

Exit code 1
The symmetric Mahler conjecture and its equality cases
OpenAI
September 22, 2026
Abstract
We resolve the symmetric Mahler conjecture positively, including its equality classification.
Every origin-symmetric convex body inRn has volume product at least4n/n!, with equality
exactly for invertible linear images of Hanner polytopes.
Contents
1 Introduction 1
1.1 History and context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.2 Proof overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.3 Functional Mahler inequality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2 Polarity, norm sums, and Hanner polytopes 5
3 A conformal lens with a uniform boundary coordinate 7
4 Mass at an isolated holomorphic zero 9
5 Feasible bases 12
5.1 Boundary equalities for the lens . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
6 The real simplex identity 14
6.1 Approximation of an arbitrary convex body . . . . . . . . . . . . . . . . . . . . . . . 16
7 Equality and a common feasibility witness 17
7.1 Inner polytopes and vanishing missing volume . . . . . . . . . . . . . . . . . . . . . . 18
7.2 From an integral deficit to each point and direction . . . . . . . . . . . . . . . . . . . 19
7.3 Comparison with every positive representation . . . . . . . . . . . . . . . . . . . . . 20
8 From endpoint costs to metric medians 21
8.1 Three balls and the equality classification . . . . . . . . . . . . . . . . . . . . . . . . 23
1 Introduction
A convex body is a compact convex subset ofRn with nonempty interior. IfK =−K, its center0
is an interior point, and its polar body is
K◦ ={y∈R n :⟨x,y⟩≤1for everyx∈K}.
1
Write|K| for n-dimensional Lebesgue volume andP (K) =|K||K◦| for the volume product. The
two determinant factors cancel under an invertible linear change of coordinates, soP (TK ) =P (K).
Mahler’s symmetric volume-product problem asks how small this linear invariant can be. The
conjectured value is4n/n!, the product of a cube and its polar cross-polytope. We prove this bound
in every dimension and classify the equality cases.
The extremal bodies are defined recursively. A centered nondegenerate interval is a one-
dimensionalHanner polytope. Given Hanner polytopes H1⊂R k and H2⊂R l, their Cartesian
product and the convex hull
H1⊕∞H2 =H 1×H 2,
H1⊕1H2 = conv
(︁
(H1×{0})∪({0}×H 2)
)︁
are Hanner polytopes inRk+l. These are theℓ∞ and ℓ1 sums, respectively. This class contains
cubes, cross-polytopes, and mixed recursive sums. We call an invertible linear image of a Hanner
polytope alinear Hanner body.
Theorem 1.1.For every integern≥1and every origin-symmetric convex bodyK⊂R n,
|K||K◦|≥ 4n
n!.(1)
Equality holds if and only ifKis a linear Hanner body.
The linear equivalence in the statement respects the fixed origin used for polarity.
1.1 History and context
Mahler introduced the volume-product problem in his work on convex bodies and transference
in the geometry of numbers [22]; the planar inequality also goes back to him [21]. Reisner later
characterized its equality cases as parallelograms, as a consequence of his zonoid theorem [28, p. 340].
Iriyeh and Shibata proved the complete three-dimensional symmetric theorem, including equality [14,
Theorem 1.1]. Fradelizi, Hubard, Meyer, Roldán-Pensado, and Zvavitch gave a shorter proof based
on equipartitions, together with another equality argument and stability [9]. Chen, Li, Xi, and Xu
recently gave a shadow-flow proof of that theorem in a preprint that also treats the nonsymmetric
three-dimensional problem [4, Theorem 7.1].
Hanner’s recursively constructed spaces [12] already play a central role in the known higher-
dimensional cases. Saint-Raymond proved the sharp inequality for unconditional bodies (those
invariant under coordinate sign changes) [30]; Meyer and Reisner identified the Hanner equality
cases in that class [23, 29]. Reisner also proved the sharp inequality for zonoids, which are Hausdorff
limits of Minkowski sums of centered segments, and showed that equality within that class means
a parallelotope [27, 28]; Gordon, Meyer, and Reisner later gave a short geometric proof [10]. For
local questions in Banach–Mazur distance, Nazarov, Petrov, Ryabogin, and Zvavitch established
strict local minimality, up to linear equivalence, of the cube [25]. Kim extended this to all Hanner
polytopes [16]. Kim and Zvavitch obtained stability in the unconditional class and the inequality in
a neighborhood of that class [17].
A separate line of work seeks dimension-independent exponential bounds without the sharp
constant. BourgainandMilmanestablishedsuchareverseBlaschke–Santalóinequality[ 3]; Kuperberg
obtained an explicit stronger bound by a geometric argument using Gauss linking integrals [18].
Complex-analytic approaches provide another setting for these questions. Nazarov used Hörmander’s
¯∂ theorem and Bergman kernels for the reverse Blaschke–Santaló inequality [24]. Berndtsson recast
2
Kuperberg’s proof using complex integrals [1]. In a symplectic approach, Karasev proved the sharp
inequality for hyperplane sections and projections ofℓp balls (1≤p≤∞ ) and Hanner polytopes [15,
Theorems 4.2 and 4.4]. These works give analytic and geometric precedents for volume-product
estimates; their estimates are not hypotheses of our proof.
The analytic ingredient is a conformal map of the disk for which uniform angular measure on the
boundary maps to a uniform imaginary coordinate. It is a rotation of Gross’s example for the uniform
distribution in his conformal Skorokhod embedding [11, Section 3.2]. A holomorphic mass estimate
turns that boundary law into a lower bound for an integral of feasible-simplex volumes over the
primal body. We prove the mass estimate directly by Stokes’ theorem, with its precise normalization;
its general background is the theory of generalized Lelong numbers [6, Sections 3 and 5]. The
symmetric-convex extremal-function framework of Lundin and Baran was a further motivation for
the analytic search; see [20] and the formula recorded in [2, Proposition 1.2, pp. 246–247]. The proof
here instead uses the direct isolated-zero mass calculation, not those extremal-function formulas
as premises. The equality argument leads to a classical property of normed spaces: any three
pairwise-intersecting closed balls have a common point. Hanner studied the associated recursively
constructed convex bodies [12]. Lima’s norm-additive decomposition criterion [19, Theorem 1.2],
also recorded in Hansen–Lima [13, Theorem 3.6(4)], is exactly the metric-median condition used
below. Reisner’s unconditional equality theorem already connects minimal volume product to this
ball intersection structure [29, Theorem 1]. Our endpoint argument obtains the structure without
assuming unconditionality; Hansen–Lima’s finite-dimensional classification is the final input [13,
Corollary 7.4].
For a body without a prescribed center, the general Mahler problem instead minimizes|K|| (K−
z)◦| overz∈intK . Its conjectured constant(n+1)n+1/(n!)2 is smaller than the symmetric constant
for n≥ 2; a result for that general problem alone therefore does not establish Theorem 1.1. The
argument here uses symmetry throughout and is independent of the general theorem proved in the
companion paper [26, Theorem 1.1].
1.2 Proof overview
Section 2 records polarity and the two norm-sum formulas. They show that every Hanner body
has the stated volume product. The remaining work is the lower bound and its converse equality
statement.
First let
A={X∈R d :|b i·X|≤1,1≤i≤m}, A ◦ = conv{±b1,...,±b m},
where the nonzero, pairwise nonproportional rowsbi span Rd. Section 3 constructs a planar lens
with horizontal half-widthλ(t)at heightt. ForX∈intA, use this width to form
LX ={Y∈R d :|b i·Y|≤λ(b i·X),1≤i≤m}.
Each independentd-tuple of signed constraints that are tight at one point ofLX determines a
simplex: take the convex hull of0and those signed rows. LetΣX be the union of these simplices.
It lies inA◦.
WriteF for the conformal map from the unit disk to the lens. For each setI of d independent
rows, sample independent uniform anglesθi and solvebiZ =F (eiθi), i∈I , for the complex vector
Z. LetPI be the probability that all remaining row coordinates also lie in the closed lens. Section 5
applies the mass estimate of Section 4 to high powers of the inverse map: after a change of radial
variables, the Jacobian integrals concentrate on these boundary samples and forceS :=∑︁
IPI≥ 1.
3
The uniform imaginary coordinate then identifies this probability sum with the simplex integral in
Section 6:
1≤S= d!
4d
∫︂
A
|ΣX|dX≤ d!
4d|A||A◦|.
The first inequality is analytic; the last is the inclusion just described. The exact identity behind
this bound also controls the missing volume|A◦|−| ΣX|. Approximation extends the inequality to

(eval):1: ====== not found

>cd /private/tmp/claude-501/-Users-karamelton/189903e3-2f4c-44dd-8020-b4552bf40364/scratchpad && head -c 7000 txt_trace.txt; echo; echo ...; grep -n -i -E 'breakthrough|key idea|insight|realiz|lens|gross|aha|finally' txt_trace.txt | head -40

Geometric Mahler conjectures 1
Summarized chain of thought (Symmetric and general Mahler
conjectures)
OpenAI
These three attempts address complementary parts of the Mahler problem. Part I concerns the sharp
symmetric lower bound. Part II takes that bound as a supplied premise and seeks the Hanner equality
classification; its supplied premise and candidate reasoning are not identified with the particular proof
in Part I. Part III develops a separate general-body argument for the simplex bound and its equality
cases, rather than deriving it from the symmetric attempts. The three parts retain separate arguments
and hypotheses.
Part I: The symmetric lower bound
Original prompt (excerpts)
# Problem
For an integer $n\ge4$, let $K\subset\mathbb R^n$ be a compact convex set with nonempty interior,
with $K=-K$ and $0$ in its interior. Define its polar by
$$
K^\circ=\{y\in\mathbb R^n:\langle x,y\rangle\le1\text{ for every }x\in K\},
\qquad \langle x,y\rangle=\sum_{i=1}^n x_i y_i.
$$
Writing $\operatorname{vol}_n$ for $n$-dimensional Lebesgue volume, is it true that
$$
\operatorname{vol}_n(K)\operatorname{vol}_n(K^\circ)\ge\frac{4^n}{n!}
$$
for every $n\ge4$ and every such $K$?
1 Boundary laws and the symmetric bound
The symmetric attempt begins by recalling the three-dimensional result of Iriyeh–Shibata, Meyer’s un-
conditional argument, and the lower bounds of Bourgain–Milman, Kuperberg, and Nazarov [ 36, 46,
15, 39, 49]. It tries section induction, face flags, equal-measure cone partitions, entropy , and tensoriza-
tion, but finds no sharp constant along these routes. In the polytope approach, it invokes Stanley’s
lower bound for the ℎ-vector of centrally symmetric simplicial polytopes, but still needs a bound for the
associated volume fractions [ 62]. It revisits Kuperberg’s neck construction and Berndtsson’s complex-
integral proof, examines Nazarov’s Bergman-kernel approach, and considers Klartag’s relation between
isotropic constants and Mahler volumes [ 39, 9, 49, 38].
Lundin’s extremal-function formula and the Baran metric lead it toward planar boundary laws and
complexified slabs [44, 5, 6]. It chooses
𝑓 (𝜁 ) = 8
𝜋2 ∑
𝑘≥1, 𝑘 odd
𝜁 𝑘
𝑘2 ,
whose boundary real part is a triangle wave, hence uniformly distributed on [−1, 1]. For each row basis
of a slab presentation of 𝐾, it defines a feasibility probability 𝑃𝐼 using independent boundary values.
Geometric Mahler conjectures 2
It uses powers of the inverse conformal map, positivity of the complex Hessian of a logarithmic po-
tential, Stokes’ theorem, and a homogeneous flux calculation to derive ∑𝐼 𝑃𝐼 ≥ 1 . For the opposite
inequality , it associates feasible bases with disjoint simplices in 𝐾∘, obtaining ∑𝐼 𝑃𝐼 ≤ 𝑛!|𝐾||𝐾 ∘|/4𝑛. It
repeatedly checks normalization, genericity , boundary ties, and Hanner examples, then presents the
argument with approximation from slab polytopes to arbitrary symmetric bodies.
Part II: Equality and Hanner polytopes
Original prompt (excerpts)
# Symmetric Mahler–Hanner: remaining equality classification
Let \(|\cdot|\) denote Lebesgue measure in \(\mathbb{R}^n\), and let \(\langle\cdot,\cdot\rangle\)
be the standard inner product. A convex body in \(\mathbb{R}^n\) is a compact convex set with
nonempty interior. Write
\[
\mathcal K_0^n=\{K\subset\mathbb{R}^n: K\text{ is a convex body and }K=-K\}.
\]
For \(K\in\mathcal K_0^n\), define its polar body by
\[
K^\circ=\{y\in\mathbb{R}^n:\langle x,y\rangle\le 1\text{ for all }x\in K\},
\]
and its volume product by
\[
P(K)=|K|\,|K^\circ|.
\]
For convex bodies \(K\subset E\) and \(L\subset F\) in finite-dimensional real vector spaces,
define their \(\ell_\infty\)- and \(\ell_1\)-sums in \(E\oplus F\) by
\[
K\oplus_\infty L=K\times L,
\qquad
K\oplus_1 L=\operatorname{conv}\bigl((K\times\{0\})\cup(\{0\}\times L)\bigr).
\]
The class of Hanner polytopes is the smallest class of origin-symmetric convex polytopes containing
all origin-symmetric line segments and closed under \(\oplus_\infty\) and \(\oplus_1\).
Open problem: For every integer n >= 4 and every origin-symmetric convex body K in \(\mathcal
K_0^n\), prove or disprove that
\[
P(K)=\frac{4^n}{n!}
\quad\Longleftrightarrow\quad
K\text{ is linearly equivalent to an }n\text{-dimensional Hanner polytope}.
\]
A negative answer must give a mathematically valid counterexample to this equivalence. A positive
answer must establish it in every dimension n >= 4.
2 A lens argument for Hanner equality
The attempt takes a previously supplied sharp symmetric lower bound as a premise and reconstructs an
earlier candidate argument for the equality case. That argument includes a lens construction and routes
from its equality conditions to the three-ball intersection property.
It first checks that polarity interchanges the two Hanner sums and that either sum in dimensions 𝑘, 𝑙
Geometric Mahler conjectures 3
has volume product 𝑃(𝐴)𝑃(𝐵)/(𝑘+𝑙
𝑘 ). Induction from segments therefore gives the Hanner direction.
For the converse, the assistant studies the analytic map
𝐹(𝑧) = 8
𝜋2 ∑
𝑗≥0
(−1)𝑗𝑧2𝑗+1
(2𝑗 + 1)2 .
It checks convexity of its inner image curves through the positivity of Re(1+𝑧𝐹 ″/𝐹′). The limiting bound-
ary has branches ±𝜆(𝑡) + 𝑖𝑡 , with uniform height under uniform phase and 𝜆″(𝑡) = − sec(𝜋𝑡/2). For
spanning real rows it forms a sum 𝑆(𝑟) of basis-feasibility probabilities. Splitting a smoothed complex
Hessian determinant into minors containing a constant row and those omitting it, it derives monotonic-
ity: the latter terms decrease with 𝑟, integration by parts makes total cutoff-mass differences tend to zero,
and the former terms converge to feasibility probabilities. It checks extra-row ties through a rank-two
phase map and uses dominated convergence at the circle and lens endpoints.
The assistant obtains the circle value 𝑆(0+) = 1 by randomizing squared radii exponentially , integrat-
ing complex Gaussians, and applying Cauchy–Binet. It also considers a deterministic explanation, but
finds phase choices for which two bases are feasible simultaneously:
VERBA TIM EXCERPT
Then some phase none to balance. Thus no deterministic. Laplace solution concise.
At the lens endpoint, the assistant bounds the total volume of the disjoint feasible simplices by the vol-
ume of the polar body. Integrating over the original body yields the symmetric lower bound 𝑃(𝐾) ≥
4𝑛/𝑛!. It then examines the equality conditions.
For an equality body the assistant lifts to 𝐾 ⊕ 1 [−1, 1], whose polar is 𝑄 × [−1, 1], where 𝑄 = 𝐾 ∘. It
uses inscribed product approximations and vanishing integrated missing volume to extract cost-optimal
representations with minimum total mass. The asymptotic 𝜆(1 − 𝜂) = (2/𝜋)𝜂 log(1/𝜂) + 𝑂(𝜂) turns
those costs into aﬀine expressions. Comparing
...
84:2 A lens argument for Hanner equality
86:earlier candidate argument for the equality case. That argument includes a lens construction and routes
104:phase map and uses dominated convergence at the circle and lens endpoints.
110:At the lens endpoint, the assistant bounds the total volume of the disjoint feasible simplices by the vol-
255:The concentration giving the uniform simplex was too large for the ball model. Finally , it expressed
277:Petty inequality gave the opposite direction from the estimate it needed [ 67]. It realized arbitrary
281:Realize proving global minima for projective zonoid by reducing to boundary leads tangent arbitrary
299:order change for degree-three harmonics. It therefore rejected its expected sign, while leaving realization
407:difference between central sections and the sections through a vertex arising here. Finally , exponential
495:and (𝐽𝜒𝐷(𝑢))1/𝑑 ∼ 𝜋/2 . For the upper estimate it finally wrote an entropy identity for the density
535:Finally , rare large scalar matrices contradicted an unrestricted reverse Cauchy–Schwarz inequality for
586:vented an immediate positive coupling of complete face chains. Finally , it used the angular partition
633:Finally it varied a weighted curvature functional. At a hypothetical minimizer, it interpreted its signed
675:tion [ 39, 7]. Finally , slicing a cone along an extreme ray produced an ordinary convex body with a
876:tube concavity , and polynomial-degree formulas [17, 24, 11]. Finally , it rewrote the calculation directly
943:Finally , the assistant separated the two centered paths altogether, prescribing primal and dual means
1031:while looking for a different integral argument [ 54, 16, 50, 39]. Finally , ellipsoidal fibers over the aﬀine-
1059:mixed signs for some proposed deformation modes; it treated their realization by actual cones as a
1101:34, 50]. Finally , it decomposed the random section’s normal cones by upper and lower active constraints.
1209:The assistant finally revisited paths that switched from primal centering to dual centering at a cutoff.
1224:spectral variances, but it still needed a calibration realizing that cancellation. Anisotropically scaled or-
1257:The assistant then proposed realizing smoothing by higher-dimensional cone lifts, hoping to turn
1402:Finally , cumulative positive matrices gave it a proposed logarithmic-determinant comparison without
1570:The assistant checked biased Gaussian projections realizing prescribed means, their tail estimates, and
1586:uct cone, an orthant, and a simplex section. Finally , it returned to complex-strip interpolation, profile
Shell cwd was reset to /Users/karamelton

{
  "type": "tool_reference",
  "tool_name": "mcp__blender__get_addon_status"
}
{
  "type": "tool_reference",
  "tool_name": "mcp__blender__get_scene_info"
}
{
  "type": "tool_reference",
  "tool_name": "mcp__blender__execute_blender_code"
}
{
  "type": "tool_reference",
  "tool_name": "mcp__blender__look"
}

Next I'm checking that Blender is connected, then I'll build the 3D shape pairs.

>cd /private/tmp/claude-501/-Users-karamelton/189903e3-2f4c-44dd-8020-b4552bf40364/scratchpad/repo/math && cat lean/docs/087.md | head -60; grep -n -B2 -A10 '087' lean/formalization.yaml | head -60

# The Mahler conjectures, functional inequalities and polar-product symplectic width

The following describes the scope of the Lean formalization related to the following accompanying paper(s):

- [The symmetric Mahler conjecture and its equality cases](../../preprints/The-symmetric-Mahler-conjecture-and-its-equality-cases-September-22-2026/paper.pdf)
- [The Mahler Conjecture for General Convex Bodies](../../preprints/The-Mahler-Conjecture-for-General-Convex-Bodies-September-22-2026/paper.pdf)
- [Symplectic Balls in Symmetric Polar Products](../../preprints/Symplectic-Balls-in-Symmetric-Polar-Products-September-22-2026/paper.pdf)

## Scope

The symmetric Mahler conjecture predicts $|K||K^\circ|\ge4^n/n!$ for every origin-symmetric convex body $K\subset\mathbb R^n$. The formalization establishes this for every $n\ge1$ and characterizes equality exactly by invertible linear images of Hanner bodies, built from intervals using Cartesian products and convex-hull joins.

The nonsymmetric Mahler conjecture and the functional inequalities are not included.

The general Mahler conjecture gives a sharp lower bound for the volume product of a convex body and its polar. For every $n\ge1$ and compact convex body $K\subset\mathbb R^n$ with nonempty interior, the formalization proves
$\inf_{z\in\mathrm{int}\,K}|K|\,|(K-z)^\circ|\ge (n+1)^{n+1}/(n!)^2$.
Equality holds exactly when $K$ is a simplex. The paper's functional inequality is outside this selected statement.

For an origin-symmetric convex body $K\subset\mathbb R^n$, $n\ge2$, the formalized result determines the symplectic ball capacity of $\mathrm{int}\,K\times\mathrm{int}\,K^\circ$: its Gromov width is $4$, and every ball of capacity $0<c<4$ embeds symplectically into it. The normalization assigns capacity $\pi r^2$ to a ball of radius $r$.

No boundary smoothness or strict convexity is assumed. An embedding at capacity exactly $4$ is not asserted.

## Comparator links

| Result | Comparator statement |
| --- | --- |
| Symmetric Mahler inequality | [MahlerConjecture.lean](../ComparatorChallenges/MahlerConjecture.lean) |
| Symmetric Mahler equality characterization | [SymmetricMahlerEquality.lean](../ComparatorChallenges/SymmetricMahlerEquality.lean) |
| General Mahler inequality and simplex equality cases | [GeneralMahler.lean](../ComparatorChallenges/GeneralMahler.lean) |
| Gromov width of symmetric polar products | [SymmetricPolar.lean](../ComparatorChallenges/SymmetricPolar.lean) |
Shell cwd was reset to /Users/karamelton

>cat /Users/karamelton/.claude/projects/-Users-karamelton/memory/feedback_confirm_file_destination.md /Users/karamelton/.claude/projects/-Users-karamelton/memory/feedback_no_files_in_repo_without_asking.md

---
name: feedback_confirm_file_destination
description: "Before creating any new file (reports, moved/renamed files, downloaded assets, scratch output, generated docs), confirm the destination path with the user first rather than picking a location unilaterally"
metadata: 
  node_type: memory
  type: feedback
  originSessionId: 0792df0e-2437-4e1d-a7ff-6e9862ea1240
  modified: 2026-09-03T00:27:55.248Z
---

Confirm the destination path with the user before creating any new file, every time — not just for files going into a git repo (see [[feedback_no_files_in_repo_without_asking]], which is the repo-specific case of this same rule).

**Why:** During a home-folder cleanup session, files ended up in unexpected or inconsistent locations (e.g. a plan doc that was supposed to land in `Traces/Builds/highlight-to-save/` was later found in `Traces/Projects/highlight-to-save/` instead, apparently moved outside the conversation). The user explicitly asked for a standing habit of confirming file locations because "I don't want things to get messy." They chose the strict option: confirm on *any* new file, not only ambiguous cases.

**How to apply:** Before writing a new file anywhere (Write tool, `mv`/`cp` to a new destination, saving a downloaded/generated asset, creating a report or scratch doc), state or ask where it's going and get explicit confirmation first — even if the destination seems obvious from context. This does not apply to editing an existing file in place (no new file/path is being created). Default to asking rather than assuming, even for locations that seem low-stakes (Desktop, scratchpad, home folder).
---
name: feedback_no_files_in_repo_without_asking
description: "Never write files (even untracked, uncommitted docs/plans) into the user's real git repos without asking first; export research/planning docs outside the repo instead"
metadata: 
  node_type: memory
  type: feedback
  originSessionId: f0746df6-700d-4874-b96a-50a23b4a5bb4
  modified: 2026-09-03T00:27:58.570Z
---

Never place a new file inside one of the user's real git repos (e.g. `~/Documents/Traces/code/traces`, moved from `~/code/traces` on 2026-09-02) without asking first — not even a purely informational, uncommitted doc like an implementation plan. Placing it there was treated as overstepping even though nothing was staged or committed. See also [[feedback_confirm_file_destination]] — the broader rule this is a special case of.

**Why:** The user pushed back hard ("no i don't want this in the repo... never push without asking me. pull it back") after I wrote a plan-mode research doc to `docs/projects/highlight-to-save.md` on my own judgment, reasoning it matched the team's own naming convention for project docs. The user's real codebase is not a scratch space — any file landing there, even untracked, is unwanted without explicit go-ahead. Note: the user's phrasing conflated "in the repo" with "push" — treat any write to a tracked repo's working tree the same as a would-be push for permission purposes, even though this instance never touched git at all.

**How to apply:** When exporting research, plans, or notes that happen to be *about* a specific repo, default to a location clearly outside it — the user's home directory, the scratchpad, or `~/.claude/plans/` — and only write into the actual repo if the user explicitly asks for it there (e.g. "put this in docs/projects/"). If unsure whether "export this" means inside or outside the repo, ask, or default outside and offer to move it in on request.

Your saved preference is to confirm where new files go, scratchpad included. One heads-up: I already cloned the repo into the session scratchpad to read the proofs, before checking that note.

Your questions have been answered: "Where should I put the surface source (one HTML file plus a small build script and the Blender render frames)?"="~/Desktop/mahler-surface". You can now continue with these answers in mind.

I'll put everything in ~/Desktop/mahler-surface/. Writing the surface template now.

Remind me how to open this in blender

To connect Blender:

Open Blender and stay in the default scene (Layout workspace).
Start the addon's server. Hover over the 3D viewport and press N to open the sidebar. Find the MCP for Blender tab and click its connect/start button. It should then show the server as running.
If you can't find the tab, the addon may not be installed or enabled. Install it with:
```
! uvx mcp-for-blender install-addon
```
```
! uvx mcp-for-blender install-addon
```
Then enable it under Edit → Preferences → Add-ons (search "MCP") and repeat step 2.

I'm going from the addon's install command and typical setup here, so the tab and button names may differ a little in your version.

Tell me once it's running. I'll check the connection, build the cube/octahedron and second shape pair, and render the turntable frames to drop into the surface. Meanwhile I'll keep building the 2D playground and the proof animation in ~/Desktop/mahler-surface/.

No content

MCP tool for blender is connected

{"result":"{\n  \"up_to_date\": false,\n  \"protocol_version\": 9,\n  \"expected_protocol_version\": 13,\n  \"addon_version\": [\n    1,\n    7\n  ],\n  \"capabilities\": [\n    \"bpy_api_lookup\",\n    \"describe_node_type\",\n    \"drain_human_activity\",\n    \"execute_code\",\n    \"get_addon_info\",\n    \"get_object_info\",\n    \"get_scene_info\",\n    \"get_telemetry_consent\",\n    \"get_viewport_screenshot\",\n    \"get_world_state_snapshot\",\n    \"set_telemetry_consent\"\n  ],\n  \"blender_version\": \"5.2.2 LTS\",\n  \"premium_generators\": [],\n  \"libraries\": {\n    \"polyhaven\": \"off\",\n    \"sketchfab\": \"off\",\n    \"polypizza\": \"off\"\n  },\n  \"generators\": {\n    \"tripo\": \"off (Premium only)\",\n    \"hunyuan3d\": \"off\",\n    \"hyper3d\": \"off\"\n  },\n  \"source\": \"native\",\n  \"warning\": \"Blender addon protocol 9 is behind expected 13. Run `uvx mcp-for-blender install-addon` to update it, then restart Blender or disable/enable 'Interface: MCP for Blender', then Start MCP Server. Trajectory still works via fallbacks.\",\n  \"telemetry_consent\": false,\n  \"update_command\": \"uvx mcp-for-blender install-addon\",\n  \"after_install\": \"If the addon file was updated: in Blender, Preferences \\u2192 Add-ons \\u2192 disable/enable 'Interface: Blender MCP', or restart Blender, then Start MCP Server.\"\n}"}

{"result":"Scene 'Scene' | 1 camera, 1 light, 1 mesh | active Cube | selected none | mode OBJECT\n(unsaved) | engine BLENDER_EEVEE | frames 1-250 (now 1) at 24 fps | 1920x1080 | camera Camera | HDRI none | unit scale 1.0\n\nShowing 3 of 3 (name | type | location | size | hidden):\nCube | mesh | at (0.0, 0.0, 0.0) | size 2.0x2.0x2.0\nLight | light | at (4.08, 1.01, 5.9)\nCamera | camera | at (7.36, -6.93, 4.96)"}

{"result":"Error executing code: Communication error with Blender: {\"exception_type\": \"IndexError\", \"message\": \"BMElemSeq[index]: outdated internal index table, run ensure_lookup_table() first\", \"traceback\": \"Traceback (most recent call last):\\n  File \\\"/Users/karamelton/Library/Application Support/Blender/5.2/scripts/addons/blender_mcp.py\\\", line 2107, in execute_code\\n    exec(code, namespace)\\n    ~~~~^^^^^^^^^^^^^^^^^\\n  File \\\"<string>\\\", line 37, in <module>\\n  File \\\"<string>\\\", line 32, in midradius\\nIndexError: BMElemSeq[index]: outdated internal index table, run ensure_lookup_table() first\\n\"}"}

{"result":"Code executed successfully: cube faces K/P 6 8 vol K 2.8284 P 3.7712 product 10.6667\nico faces K/P 20 12 vol K 4.1202 P 3.4164 product 14.0764\n4^3/3! = 10.666666666666666  ball^2 = 17.54596337971441\n"}

{"result":"Code executed successfully: ['AVIF', 'JPEG', 'OPEN_EXR', 'PNG', 'WEBP', 'BMP', 'CINEON', 'DPX', 'IRIS', 'JPEG2000', 'HDR', 'TARGA', 'TARGA_RAW', 'TIFF', 'OPEN_EXR_MULTILAYER', 'FFMPEG']\nok\n"}

{
  "type": "image",
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    "media_type": "image/png",
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"
  }
}
[Image: source: /Users/karamelton/.claude/projects/-Users-karamelton/189903e3-2f4c-44dd-8020-b4552bf40364/tool-results/mcp-blender-blob-1791769091055-yf0tyh.png]
Through camera 'Camera'. Framed 5 objects, 2.84 x 2.84 x 2.84 m across, centred at [0.0, 0.0, 0.0].

The cube/octahedron compound renders correctly. I'm zooming in and thickening the edges, then I'll render the turntable frames.

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"
  }
}
[Image: source: /Users/karamelton/.claude/projects/-Users-karamelton/189903e3-2f4c-44dd-8020-b4552bf40364/tool-results/mcp-blender-blob-1791769099322-clyyqi.png]
Through camera 'Camera'. Framed 5 objects, 3.89 x 3.89 x 2.85 m across, centred at [0.0, 0.0, 0.0].

Fast, about 6 seconds for 36 frames. Now the icosahedron/dodecahedron pair.

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"
  }
}
[Image: source: /Users/karamelton/.claude/projects/-Users-karamelton/189903e3-2f4c-44dd-8020-b4552bf40364/tool-results/mcp-blender-blob-1791769120142-05i2mt.png]
Through camera 'Camera'. Framed 5 objects, 2.59 x 2.59 x 2.02 m across, centred at [-0.0, -0.0, 0.0].

Rendering is done: 72 frames, about 770 KB. Now the main piece, the surface HTML.

>cd ~/Desktop/mahler-surface && python3 - <<'EOF'
p='template.html'; s=open(p).read()
rep=[
("background: repeating-linear-gradient(45deg, var(--border-strong) 0 3px, transparent 3px 6px); }","background: var(--border-strong); opacity: .6; }"),
("Take any blob-free shape: no dents, and in the first mode, symmetric about its center.","Draw a shape with no dents (mathematicians say <i>convex</i>) that looks the same when spun halfway around its center."),
("""In 4D a new kind of minimizer appears (<span class="mono">square ⊕ square</span>, a "Hanner polytope"), and the papers show these mixtures are the only shapes that tie the cube.""","""From 4D on, new minimizers appear, built by mixing "stack" and "glue" operations (for example <span class="mono">square ⊕₁ square</span>). These are the Hanner polytopes, and the papers show they are the only shapes that tie the cube."""),
("""Why this helps: the shape is written as a stack of slabs, one per pair of opposite faces. The proof feeds random points from the lens into each slab and asks how often they all fit at once. A complex-analysis argument shows those chances add up to <b style="font-weight:500">at least 1</b>. Because the heights are perfectly uniform, the same sum is exactly the twin's volume, scaled by <span class="mono">n!/4ⁿ</span>. Together: volume × twin's volume ≥ 4ⁿ/n!.""","""Why this helps: the shape is described by slabs, one for each pair of opposite faces. The proof picks random points on the lens's edge and asks how often they line up into a point that fits inside every slab at once. A complex-analysis argument (Stokes' theorem applied to the lens map) shows those chances add up to <b style="font-weight:500">at least 1</b>. Because the heights are perfectly even, the same sum can be rewritten as volumes of pieces of the twin, and those total at most <span class="mono">|K|·|K°|·n!/4ⁿ</span>. Together: <span class="mono">|K|·|K°| ≥ 4ⁿ/n!</span>."""),
("S.free.map(([x, y]) => [-x, y === 0 ? 0 : -y])","S.free.map(([x, y]) => [-x, -y])"),
]
for a,b in rep:
    assert a in s, a[:50]; s=s.replace(a,b)
open(p,'w').write(s)
EOF
cat > build.py <<'EOF'
"""Inline Blender turntable frames into template.html -> surface.html."""
import base64, json, os, sys
here = os.path.dirname(os.path.abspath(__file__))
def frames(d):
    fd = os.path.join(here, "frames", d)
    return ["data:image/webp;base64," + base64.b64encode(open(os.path.join(fd, n), "rb").read()).decode()
            for n in sorted(os.listdir(fd)) if n.endswith(".webp")]
TT = [
    {"title": "Cube and octahedron", "product": "10.667", "caption": "On the floor: 4³/3! = 10.667. The cube's twin is the octahedron.", "frames": frames("cube")},
    {"title": "Icosahedron and dodecahedron", "product": "14.076", "caption": "Rounder, so higher: 14.076. The ball's ceiling is 17.546.", "frames": frames("ico")},
]
s = open(os.path.join(here, "template.html")).read()
s = s.replace("/*TURNTABLES*/", json.dumps(TT).replace("</", "<\\/"))
open(os.path.join(here, "surface.html"), "w").write(s)
print("surface.html", round(len(s.encode()) / 1024), "KB")
EOF
python3 build.py

surface.html 2107 KB

Bug: the build's placeholder string also appears inside the JS, so the frame data would be injected there too. Fixing that.

>cd ~/Desktop/mahler-surface && python3 - <<'EOF'
p='template.html'; s=open(p).read()
a='''try { TT = JSON.parse($("tt-data").textContent.replace("/*TURNTABLES*/", "") || "[]"); } catch (e) { TT = []; }'''
b='''try { const raw = $("tt-data").textContent.trim(); TT = raw.startsWith("[") ? JSON.parse(raw) : []; } catch (e) { TT = []; }'''
assert a in s; s=s.replace(a,b); assert s.count("/*TURNTABLES*/")==1
open(p,'w').write(s)
EOF
python3 build.py && grep -c 'data:image/webp' surface.html && node -e "
const s=require('fs').readFileSync('surface.html','utf8');
const m=[...s.matchAll(/<script(?![^>]*json)[^>]*>([\s\S]*?)<\/script>/g)];
for(const x of m){ new Function(x[1]); } console.log('js parses:', m.length);
const j=s.match(/<script id=\"tt-data\"[^>]*>([\s\S]*?)<\/script>/)[1]; console.log('frames', JSON.parse(j).map(t=>t.frames.length));
for (const bad of ['http:','https:','<link','@import','window.open','location']) if (s.includes(bad)) console.log('contains', bad);
"

surface.html 1073 KB
1
js parses: 1
frames [ 36, 36 ]

>cd ~/Desktop/mahler-surface && node -e "
const s=require('fs').readFileSync('template.html','utf8');
const js=s.match(/\/\* ---------------- geometry ---------------- \*\/([\s\S]*?)\/\* ---------------- playground state/)[1];
eval(js+\`
const prod=(pts,gen)=>{const h=hull(pts).map(i=>pts[i]);const c=gen?santalo(h).z:[0,0];return area(h)*area(polar(h,c))};
console.log('square',prod([[1,1],[-1,1],[-1,-1],[1,-1]]));
console.log('hexagon',prod(ngon(6,1.15,Math.PI/6)));
console.log('48-gon',prod(ngon(48,1.05)), 'pi^2', Math.PI**2);
console.log('triangle gen',prod(ngon(3,1.3,Math.PI/2),true));
console.log('offcenter triangle gen',prod([[0,0],[3,0],[0.5,2]],true));
console.log('square gen',prod([[1,1],[-1,1],[-1,-1],[1,-1]],true));
console.log('stretched sq',prod([[3,1],[-3,1],[-3,-1],[3,-1]]));
\`)"

square 8
hexagon 9
48-gon 9.855519697370408 pi^2 9.869604401089358
triangle gen 6.75
offcenter triangle gen 6.75
square gen 8
stretched sq 8

All values match the theory, including an off-center triangle, which shows the Santaló-point search works. Now uploading to Traces.

{"query":"surface upload","results":[{"name":"traces_surfaces_complete_upload","description":"Step 2 of 2 to publish surface HTML: finalize an uploaded artifact as an immutable surface version. The result includes a previewUrl that renders this exact version on the user's latest trace (namespace members only) so they can try it before it goes live; always share it with the user. Make it current with release: true here or, after the user has tried it, via traces_surfaces_release_version, which also controls visibility.","inputSchema":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"surface":{"type":"object","properties":{"key":{"type":"string","minLength":1}},"required":["key"],"additionalProperties":false},"version":{"type":"string","minLength":1},"artifactId":{"type":"string","minLength":1,"description":"data.artifactId from the upload response."},"release":{"default":false,"description":"Make this version current immediately. Default false: the version is uploaded but users keep seeing the current one until traces_surfaces_release_version.","type":"boolean"}},"required":["surface","version","artifactId","release"],"additionalProperties":false},"annotations":{"readOnlyHint":false,"destructiveHint":true,"idempotentHint":false,"openWorldHint":true}},{"name":"traces_surfaces_prepare_upload","description":"Step 1 of 2 to publish surface HTML: reserve a version and get a single-use upload destination, creating the surface first if the key is new (pass name). Send the raw HTML file body to the returned url with the returned method and headers (for example: curl -fsS -X POST -H 'Content-Type: text/html' --data-binary @surface.html <url>); the response contains data.artifactId. Then call traces_surfaces_complete_upload. The destination expires quickly and needs no bearer token. Never pass HTML through this tool. Uploading does not change what users see: complete_upload leaves the current version untouched and returns a previewUrl for trying the new one.","inputSchema":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"surface":{"type":"object","properties":{"key":{"type":"string","minLength":1}},"required":["key"],"additionalProperties":false},"name":{"description":"Display name. Required only when the surface key does not exist yet; creates it.","type":"string","minLength":1},"description":{"type":"string"},"icon":{"type":"string"},"version":{"type":"string","minLength":1,"description":"New version label, e.g. 1.0.0. Must not already exist."},"byteSize":{"type":"integer","exclusiveMinimum":0,"maximum":9007199254740991,"description":"Exact byte size of the HTML file (wc -c). The upload is rejected if it differs."}},"required":["surface","version","byteSize"],"additionalProperties":false},"annotations":{"readOnlyHint":false,"destructiveHint":false,"idempotentHint":false,"openWorldHint":true}},{"name":"traces_surfaces_create","description":"Create a new private surface in this connection's namespace. The new surface has no uploaded versions or current version.","inputSchema":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"key":{"type":"string","minLength":1},"name":{"type":"string","minLength":1},"description":{"type":"string"},"icon":{"type":"string"}},"required":["key","name"],"additionalProperties":false},"annotations":{"readOnlyHint":false,"destructiveHint":false,"idempotentHint":false,"openWorldHint":true}},{"name":"traces_surfaces_release_version","description":"Release an already uploaded surface version as current, or change visibility. The version must be completed through traces_surfaces_complete_upload first. This operation does not accept HTML or upload files. Omit visibility to preserve the current visibility; omission never implies public visibility.","inputSchema":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"surface":{"type":"object","properties":{"key":{"type":"string","minLength":1}},"required":["key"],"additionalProperties":false},"version":{"type":"string","minLength":1},"visibility":{"type":"string","enum":["private","public"]}},"required":["surface","version"],"additionalProperties":false},"annotations":{"readOnlyHint":false,"destructiveHint":true,"idempotentHint":true,"openWorldHint":true}},{"name":"traces_surfaces_archive","description":"Archive an existing surface. Archived surfaces remain in version history but are excluded from normal discovery and public availability.","inputSchema":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"surface":{"type":"object","properties":{"key":{"type":"string","minLength":1}},"required":["key"],"additionalProperties":false}},"required":["surface"],"additionalProperties":false},"annotations":{"readOnlyHint":false,"destructiveHint":true,"idempotentHint":true,"openWorldHint":true}},{"name":"traces_surfaces_get","description":"Get the complete management record for an existing surface, including metadata, current version, and immutable version history.","inputSchema":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"surface":{"type":"object","properties":{"key":{"type":"string","minLength":1}},"required":["key"],"additionalProperties":false}},"required":["surface"],"additionalProperties":false},"annotations":{"readOnlyHint":true,"destructiveHint":false,"idempotentHint":true,"openWorldHint":true}},{"name":"traces_surfaces_restore","description":"Restore an archived surface without changing its current version or visibility.","inputSchema":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"surface":{"type":"object","properties":{"key":{"type":"string","minLength":1}},"required":["key"],"additionalProperties":false}},"required":["surface"],"additionalProperties":false},"annotations":{"readOnlyHint":false,"destructiveHint":false,"idempotentHint":true,"openWorldHint":true}},{"name":"traces_surfaces_search","description":"List surfaces in this connection's namespace, including private, archived, and unapproved surfaces. Use this to discover a surface key before performing mutations.","inputSchema":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"query":{"type":"string"},"includeArchived":{"default":false,"type":"boolean"},"limit":{"default":50,"type":"integer","minimum":1,"maximum":200}},"required":["includeArchived","limit"],"additionalProperties":false},"annotations":{"readOnlyHint":true,"destructiveHint":false,"idempotentHint":true,"openWorldHint":true}}]}

{"surface":{"key":"mahler-shadow-twin"},"created":true,"version":"0.1.0","upload":{"url":"https://actions.traces.com/v1/internal/surface-artifact-uploads/surface_upload_4b76bd3be3aad834cc9fa89894f5fc26bdb19197419037afc7bc7e40e8f665c1","method":"POST","headers":{"Content-Type":"text/html"}},"nextStep":"Upload the file, then call traces_surfaces_complete_upload with version \"0.1.0\" and the returned artifactId."}

>curl -fsS -X POST -H 'Content-Type: text/html' --data-binary @/Users/karamelton/Desktop/mahler-surface/surface.html https://actions.traces.com/v1/internal/surface-artifact-uploads/surface_upload_4b76bd3be3aad834cc9fa89894f5fc26bdb19197419037afc7bc7e40e8f665c1

{"ok":true,"data":{"artifactId":"surface_artifact_72477f7e90cb5a54cd7af2cb1d5160bceaec68d2de8c2f932a5c721337ad7a74"}}

{"surface":{"createdAt":1791769346819,"createdBy":"kd7ekkrfd435k459fw0hnpgq3187e6sg","currentVersion":null,"description":"Interactive explainer for the Mahler conjecture (openai/math family 087): drag shapes, watch their polar twins, see 3D Blender renders and the proof's lens trick.","id":"ph74gt3rfy7fdwqfp5rk2psabd8g553f","key":"mahler-shadow-twin","name":"Mahler: shadow twins","namespaceId":"k977fb4w5km4539pw9bsq3dm4d87ffwc","publishStatus":"private","updatedAt":1791769346819,"versions":[{"approvalStatus":"not_requested","createdAt":1791769356833,"htmlByteSize":1099170,"htmlSha256":"b9b00251123c1a11d588abb80898fb34de450c760162f883a1ae7e6939094fde","sdkVersion":"surface-sdk.v1","version":"0.1.0","sourceUrl":null}],"canManage":true,"icon":null,"archivedAt":null},"version":"0.1.0","released":false,"previewUrl":"https://traces.com/s/52797cc3-0c4f-4505-b3fa-3bb171d53396?surface=mahler-shadow-twin&version=0.1.0","nextStep":"Ask the user to try previewUrl; when happy, call traces_surfaces_release_version with version \"0.1.0\" to make it current."}

Request interrupted by user

wait... what are you uploaded to traces? As I think this through a surface can only be opened if there's a trace to apply it to so it wouldn't be possible to use this surface... tho useful to have the structure of the file as you've built it

You're right, and I should have raised it before uploading. A surface is a lens on one trace's data, and this page never reads the trace. On any trace it shows the same standalone explainer, so it's in the wrong container.

What I uploaded: one new surface in your goldfishandswedishfish namespace:

Key mahler-shadow-twin, version 0.1.0, about 1.1 MB.
Private and not released, so no one sees it on any trace. The only way to view it is the preview link, which renders it on your latest trace.
Nothing else was changed or published. I can archive it whenever you like (archived surfaces stay in version history but drop out of discovery).

What's reusable, all in ~/Desktop/mahler-surface/:

template.html: the page, with the 2D playground, the lens animation and the dimension table.
frames/: the 72 Blender turntable renders.
build.py: inlines the renders into the page as one self-contained surface.html.

The geometry is checked: square = 8, hexagon = 9, triangle = 6.75, and the Blender cube/octahedron pair comes out at exactly 4³/3!.

Two directions that fit what you're after:

Publish it as a standalone page. Make it a shareable claude.ai Artifact, or host it elsewhere. The file already runs offline with no dependencies, so this is mostly a re-wrap, and each proof becomes its own explorable page. This matches "help people who've never heard of these concepts".
Make it a real trace surface. The repo ships reasoning_traces/, which are summaries of how the model worked through each proof. If one were imported as a trace, a surface could actually read it: a map of the routes the model tried, its dead ends and what finally worked. The interactive explainers would sit beside each step as context. That uses Traces for what it's good at, but importing a PDF summary as a trace may not be supported, and it's a bigger build.

Which would you like: option 1, option 2, or both (start with the standalone page, then decide on the trace version)? And should I archive the mahler-shadow-twin surface now?

No content

Give me the preview link

Here's the preview link: https://traces.com/s/52797cc3-0c4f-4505-b3fa-3bb171d53396?surface=mahler-shadow-twin&version=0.1.0

It opens version 0.1.0 on your latest trace. Only members of your namespace can open it, and it stays unreleased until you say otherwise.

The trace underneath has nothing to do with the page, since the page doesn't read it. You'll see:

Symmetric / Any shape modes: drag corners, double-click to add one, and hover edges to see the matching parts of the twin.
3D turntables: drag to rotate them.
Lens animation: "Plain circle" switches to the comparison.

No content

Oh this is interesting... the surface still works even tho it's sitting on a trace that doesn't have relevant info, right?

Yes. A surface is just an HTML page in a sandboxed iframe. The host offers trace data through window.traces.getTrace(), but nothing forces the page to use it. This page makes that call once so the handshake completes, then ignores the result, so it renders the same on any trace in your namespace.

That suggests three things:

Traces works as a host for standalone pages. Any self-contained HTML (explainers, demos, tools) can live as a surface and open as a tab next to Highlights and Full Trace. You get the sandboxing, versioning, previews and namespace access control for free.
The limit is discoverability. It only appears where someone opens a trace and picks the tab. A trace about something else is an odd front door, and the page will look out of place to anyone who finds it there.
The interesting middle ground is a page that works either way. It can stand alone, then get richer when the trace is relevant. For example, if the trace mentions "Mahler", "polar" or "Hanner", it could show those moments next to the playground: "here's where the agent computed this." On an unrelated trace it falls back to the plain explainer, the way it does now.

That last pattern could work for every proof in the repo: one explainer per proof that gets smarter on traces of someone working with it. Want me to add that trace-aware layer to this one as a test?

No content

What I think would be useful in this trace is to add a explainer at the top re: what the major arguments of the proof. For someone who hasn't deeply explored this help them understand the signifigance of the argument

I'll add an overview at the top in two parts. The first is a short orientation (the question, why it matters, why it was hard). The second walks through the proof's five major moves, each in plain language paired with the paper's technical term. It ends with the other routes in the family, how the model found the proof, and its verification status.

>cd ~/Desktop/mahler-surface && python3 - <<'EOF'
p='template.html'; s=open(p).read()

css_anchor = "/* playground */"
css = """/* overview */
.lede { font-size: 16px; letter-spacing: -0.01em; max-width: 68ch; margin-bottom: 18px; }
.why { display: grid; grid-template-columns: repeat(3, minmax(0, 1fr)); gap: 12px; }
.why .card { padding: 14px; }
.why h3, .step h3 { font-size: 13px; font-weight: 500; margin: 0 0 4px; }
.why p { font-size: 13px; color: var(--muted-foreground); margin: 0; }
.steps { list-style: none; margin: 14px 0 0; padding: 0; border-top: 1px solid var(--hairline); }
.step { display: grid; grid-template-columns: 28px minmax(0, 1.5fr) minmax(0, 1fr); gap: 4px 16px; padding: 12px 0; border-bottom: 1px solid var(--hairline); }
.step .n { font-family: var(--mono); font-size: 13px; color: var(--primary); padding-top: 1px; }
.step p { font-size: 13px; margin: 0; }
.step .term { font-family: var(--mono); font-size: 12px; color: var(--muted-foreground); line-height: 1.5; }
.step .see { font-size: 12px; color: var(--primary); margin-top: 4px; }
.aside { display: grid; grid-template-columns: repeat(3, minmax(0, 1fr)); gap: 16px; margin-top: 18px; }
.aside p { font-size: 13px; color: var(--muted-foreground); margin: 0; }
.aside h3 { font-size: 12px; font-weight: 400; color: var(--foreground); margin: 0 0 4px; }

"""
assert css_anchor in s; s = s.replace(css_anchor, css + css_anchor, 1)

mq_anchor = "@media (max-width: 860px) {"
s = s.replace(mq_anchor, mq_anchor + "\n  .why, .aside { grid-template-columns: 1fr; }\n  .step { grid-template-columns: 28px minmax(0, 1fr); }\n  .step .term { grid-column: 2; }", 1)

overview = """<!-- ============ 0. OVERVIEW ============ -->
<section id="overview">
  <p class="lede">Mahler's conjecture asks a simple-sounding question: which shapes are the <em>least round</em>? It had been open since 1939. In September 2026, an OpenAI model produced proofs that settle it in every dimension. The answer is the cube and its relatives, and the papers also pin down exactly which shapes tie for last.</p>

  <div class="why">
    <div class="card">
      <h3>The question</h3>
      <p>Every convex shape has a twin, its <i>polar</i>. A big shape has a small twin, so multiplying their volumes gives a fair score for the shape's form, not its size. The ball scores highest. Mahler guessed the cube scores lowest: 4ⁿ/n! in n dimensions.</p>
    </div>
    <div class="card">
      <h3>Why it matters</h3>
      <p>This score is one of the basic measurements of a shape. It shows up in counting lattice points (Mahler's original motivation), in the geometry of normed spaces, and in symplectic geometry. Bourgain and Milman (1987) proved the true minimum is within an exponential factor of the guess. Nobody could pin down the exact value.</p>
    </div>
    <div class="card">
      <h3>Why it was hard</h3>
      <p>Most "find the best shape" proofs smooth a shape step by step toward a single winner. Here there is no single winner: the cube, the octahedron and, from 4D on, many mixtures of the two all tie. Before these papers, only dimensions 2 and 3 and special families of shapes were settled.</p>
    </div>
  </div>

  <h2 style="margin-top:26px">How the proof works, in five moves</h2>
  <p class="muted" style="font-size:13px">Plain-language version on the left. The paper's own terms are on the right for anyone who wants to look them up.</p>
  <ol class="steps">
    <li class="step"><span class="n">1</span>
      <div><h3>Build the shape out of slabs</h3><p>A slab is the region between two parallel walls. Any symmetric shape can be approximated as closely as you like by stacking slabs. Its twin is then the shape whose corners point in the slabs' directions, so each wall of the shape becomes a corner of the twin.</p><div class="see">Try it: hover an edge in the playground below.</div></div>
      <div class="term">Approximation by slab polytopes<br>A = {x : |bᵢ·x| ≤ 1}<br>A° = conv{±bᵢ}</div></li>
    <li class="step"><span class="n">2</span>
      <div><h3>Build a fair measuring device</h3><p>The proof uses one special curve, a lens. Send a point around a circle at steady speed and push it through the map. Its height comes out perfectly evenly spread. An ordinary circle doesn't do this.</p><div class="see">See it: "The trick at the heart of the proof", below.</div></div>
      <div class="term">Conformal map of the disk onto a lens with uniform boundary height (a rotation of Gross's example)</div></li>
    <li class="step"><span class="n">3</span>
      <div><h3>Turn geometry into chance</h3><p>For each way of choosing n of the slabs, pick random points on the lens's edge and combine them into one point. Ask: how likely is it to fit inside every other slab too? An argument from complex analysis forces these chances to add up to at least 1.</p></div>
      <div class="term">Mass at an isolated holomorphic zero, by Stokes' theorem<br>S = Σ<sub>I</sub> P<sub>I</sub> ≥ 1</div></li>
    <li class="step"><span class="n">4</span>
      <div><h3>Turn chance back into volume</h3><p>Because the lens's heights are perfectly even, each chance equals the volume of a small pointed piece (a simplex) sitting inside the twin. The pieces never overlap, so together they're no bigger than the twin. Chain the two facts: 1 ≤ the chances ≤ volume × twin's volume × n!/4ⁿ.</p></div>
      <div class="term">Real simplex identity<br>1 ≤ S = (n!/4ⁿ)∫<sub>A</sub>|Σ<sub>X</sub>| dX<br>&nbsp;&nbsp;≤ (n!/4ⁿ)|A||A°|</div></li>
    <li class="step"><span class="n">5</span>
      <div><h3>Find every tie</h3><p>If a shape lands exactly on the floor, every inequality above must be perfectly tight. Tracing that back gives a concrete property: in the shape's own notion of distance, any three balls that pairwise overlap share a common point. A classical theorem says only Hanner polytopes (cube-octahedron mixtures) have it.</p><div class="see">See them: "Up a dimension", below.</div></div>
      <div class="term">Three-ball intersection property → metric medians → Hansen–Lima classification</div></li>
  </ol>

  <div class="aside">
    <div><h3>Two more routes in the same family</h3><p>Without symmetry, triangles (simplices) are the minimizers, with floor (n+1)ⁿ⁺¹/(n!)². A separate paper proves this. A third paper reaches the symmetric result through symplectic geometry: K × K° always fits a "rigid" ball whose volume approaches 4ⁿ/n!.</p></div>
    <div><h3>How the model got there</h3><p>Its reasoning summary shows it trying and setting aside a run of known approaches: induction on slices, entropy, Kuperberg's construction, Nazarov's Bergman kernels. A formula of Lundin then pointed it toward "boundary laws" on a planar curve, which became the lens in move 2.</p></div>
    <div><h3>How sure is this?</h3><p>The repository reports Lean formalizations of both the symmetric and general theorems. Lean is a proof checker that verifies each step mechanically. The repo also cautions that results may still contain issues while review continues.</p></div>
  </div>
</section>

"""
a = "<!-- ============ 1. PLAYGROUND ============ -->\n<section id=\"play\">\n"
assert a in s
s = s.replace(a, overview + a + "  <h2>Try it: a shape and its twin</h2>\n", 1)

old = """  <p class="muted">Multiply the shape's area by its twin's area. In 1939 Kurt Mahler asked how small that product can get. The answer in the plane was known; in every higher dimension it stayed open for 87 years. Drag the corners and try to get under the floor.</p>"""
new = """  <p class="muted">Multiply the shape's area by its twin's area. Mahler himself proved the floor in the flat plane: 8 for symmetric shapes. Drag the corners and try to get under it. Then switch to "Any shape" to see where triangles bottom out.</p>"""
assert old in s; s = s.replace(old, new)
open(p,'w').write(s)
EOF
python3 build.py && node -e "
const s=require('fs').readFileSync('surface.html','utf8');
for(const x of [...s.matchAll(/<script(?![^>]*json)[^>]*>([\s\S]*?)<\/script>/g)]) new Function(x[1]);
console.log('ok', s.length)"; wc -c < surface.html

surface.html 1080 KB
ok 1105886
 1106006

{"surface":{"key":"mahler-shadow-twin"},"created":false,"version":"0.2.0","upload":{"url":"https://actions.traces.com/v1/internal/surface-artifact-uploads/surface_upload_b8a1ecee4cdda63aa7437d4975c27dea897b92daea4ddcf515d9ec08f055fa7f","method":"POST","headers":{"Content-Type":"text/html"}},"nextStep":"Upload the file, then call traces_surfaces_complete_upload with version \"0.2.0\" and the returned artifactId."}

>curl -fsS -X POST -H 'Content-Type: text/html' --data-binary @/Users/karamelton/Desktop/mahler-surface/surface.html https://actions.traces.com/v1/internal/surface-artifact-uploads/surface_upload_b8a1ecee4cdda63aa7437d4975c27dea897b92daea4ddcf515d9ec08f055fa7f

{"ok":true,"data":{"artifactId":"surface_artifact_4edba8d34822f7315034c08784ea7cf4d27508c79ec546ed475043bd4bf15c5f"}}

{"surface":{"createdAt":1791769346819,"createdBy":"kd7ekkrfd435k459fw0hnpgq3187e6sg","currentVersion":null,"description":"Interactive explainer for the Mahler conjecture (openai/math family 087): drag shapes, watch their polar twins, see 3D Blender renders and the proof's lens trick.","id":"ph74gt3rfy7fdwqfp5rk2psabd8g553f","key":"mahler-shadow-twin","name":"Mahler: shadow twins","namespaceId":"k977fb4w5km4539pw9bsq3dm4d87ffwc","publishStatus":"private","updatedAt":1791769346819,"versions":[{"approvalStatus":"not_requested","createdAt":1791769356833,"htmlByteSize":1099170,"htmlSha256":"b9b00251123c1a11d588abb80898fb34de450c760162f883a1ae7e6939094fde","sdkVersion":"surface-sdk.v1","version":"0.1.0","sourceUrl":null},{"approvalStatus":"not_requested","createdAt":1791769913058,"htmlByteSize":1106006,"htmlSha256":"8e72660217b6565380aea2346430232d167587061648863d0095d1c2312f0030","sdkVersion":"surface-sdk.v1","version":"0.2.0","sourceUrl":null}],"canManage":true,"icon":null,"archivedAt":null},"version":"0.2.0","released":false,"previewUrl":"https://traces.com/s/52797cc3-0c4f-4505-b3fa-3bb171d53396?surface=mahler-shadow-twin&version=0.2.0","nextStep":"Ask the user to try previewUrl; when happy, call traces_surfaces_release_version with version \"0.2.0\" to make it current."}

I added the overview at the top and uploaded it as version 0.2.0: https://traces.com/s/52797cc3-0c4f-4505-b3fa-3bb171d53396?surface=mahler-shadow-twin&version=0.2.0

It's still private and unreleased. I checked that the file builds and its scripts parse, but I haven't viewed the new section in a browser, so look for layout problems when you open it.

What's new at the top:

A one-paragraph opener: the question is which shapes are the "least round". It was open since 1939, and these papers settle it in every dimension, including exactly which shapes tie.
Three short cards:
- The question: a shape, its twin, and multiplying their volumes as a fair score of form.
- Why it matters: where the score shows up (lattice points, normed spaces, symplectic geometry), and that before this only an approximate bound was known (Bourgain–Milman 1987).
- Why it was hard: many shapes tie for last, so the usual "smooth toward one winner" proofs don't work.
The proof in five moves. Each has a plain-language explanation, with the paper's own term beside it in small monospace:
1. Build the shape out of slabs.
2. Build a fair measuring device (the lens).
3. Turn geometry into chance: the probabilities sum to at least 1.
4. Turn chance back into volume, which gives the bound.
5. Find every tie, which leads to the Hanner polytopes.
Moves 1, 2 and 5 point to the interactive section below that shows them.
Three closing notes:
- The other two routes in the family: the non-symmetric triangle version, and the symplectic-geometry proof.
- How the model got there, drawn from its reasoning summary: the approaches it set aside before the lens idea.
- How sure this is: the repo reports Lean verification and also cautions that results may still contain issues.

The playground below now has its own heading. I trimmed its intro so it doesn't repeat the overview.

One accuracy note: I wrote the five moves from the paper's abstract and its two-page proof overview, not the full 26 pages. They match what the paper says it does, but they're a simplification, and the page's footer says so.

No content

Let's make the 'how the proof works section' roll up able so that the user doesn't have to scroll so far to try the shape, let them fold that up

>cd ~/Desktop/mahler-surface && python3 - <<'EOF'
p='template.html'; s=open(p).read()
rep = [
# open the fold
("""  <h2 style="margin-top:26px">How the proof works, in five moves</h2>
  <p class="muted" style="font-size:13px">Plain-language version on the left. The paper's own terms are on the right for anyone who wants to look them up.</p>""",
"""  <details class="fold" id="proof-fold">
  <summary><span class="chev" aria-hidden="true"></span><span class="fold-title">How the proof works, in five moves</span><span class="fold-hint">show</span></summary>
  <p class="muted" style="font-size:13px; margin-top:6px">Plain-language version on the left. The paper's own terms are on the right for anyone who wants to look them up.</p>"""),
# close it after the aside
("""    <div><h3>How sure is this?</h3>""", """    <div><h3>How sure is this?</h3>"""),
]
for a,b in rep:
    assert a in s, a[:40]; s = s.replace(a,b,1)
end = """The repo also cautions that results may still contain issues while review continues.</p></div>
  </div>
"""
assert end in s; s = s.replace(end, end + "  </details>\n", 1)

css = """/* fold */
details.fold { margin: 22px 0 0; font-size: 14px; color: var(--foreground); border-top: 1px solid var(--hairline); border-bottom: 1px solid var(--hairline); }
details.fold > summary { list-style: none; display: flex; align-items: center; gap: 10px; padding: 12px 0; cursor: pointer; user-select: none; }
details.fold > summary::-webkit-details-marker { display: none; }
details.fold > summary:hover { background: var(--accent); }
.fold-title { font-size: 18px; letter-spacing: -0.015em; }
.fold-hint { margin-left: auto; font-size: 12px; color: var(--muted-foreground); }
details.fold[open] .fold-hint::after { content: ""; }
.chev { width: 7px; height: 7px; border-right: 1.5px solid var(--muted-foreground); border-bottom: 1.5px solid var(--muted-foreground); transform: rotate(-45deg); margin: 0 2px; flex: none; }
details.fold[open] .chev { transform: rotate(45deg); }
details.fold[open] { padding-bottom: 18px; }
details.fold .steps { margin-top: 10px; }
@media (prefers-reduced-motion: no-preference) { .chev { transition: transform 120ms; } }

"""
s = s.replace("/* playground */", css + "/* playground */", 1)

# toggle hint text
js_anchor = "/* ---------------- boot ---------------- */"
js = """/* ---------------- proof fold ---------------- */
const fold = $("proof-fold");
const syncFold = () => { fold.querySelector(".fold-hint").textContent = fold.open ? "hide" : "5 moves · show"; };
fold.addEventListener("toggle", syncFold); syncFold();

"""
s = s.replace(js_anchor, js + js_anchor, 1)
open(p,'w').write(s)
EOF
python3 build.py && node -e "
const s=require('fs').readFileSync('surface.html','utf8');
for(const x of [...s.matchAll(/<script(?![^>]*json)[^>]*>([\s\S]*?)<\/script>/g)]) new Function(x[1]);
const o=(s.match(/<details/g)||[]).length,c=(s.match(/<\/details>/g)||[]).length; console.log('details open/close',o,c)"; wc -c < surface.html

surface.html 1082 KB
details open/close 2 2
 1107488

{"surface":{"key":"mahler-shadow-twin"},"created":false,"version":"0.3.0","upload":{"url":"https://actions.traces.com/v1/internal/surface-artifact-uploads/surface_upload_76f02ce3252946c6eb801969283430b4685ed35c116d9ddaa324d4f4506b9757","method":"POST","headers":{"Content-Type":"text/html"}},"nextStep":"Upload the file, then call traces_surfaces_complete_upload with version \"0.3.0\" and the returned artifactId."}

>curl -fsS -X POST -H 'Content-Type: text/html' --data-binary @/Users/karamelton/Desktop/mahler-surface/surface.html https://actions.traces.com/v1/internal/surface-artifact-uploads/surface_upload_76f02ce3252946c6eb801969283430b4685ed35c116d9ddaa324d4f4506b9757

{"ok":true,"data":{"artifactId":"surface_artifact_05e9a8d0db5b721077ed3446d3b19ab7c5c59319d3f15b9fc2655f8d4190ad1d"}}

{"surface":{"createdAt":1791769346819,"createdBy":"kd7ekkrfd435k459fw0hnpgq3187e6sg","currentVersion":null,"description":"Interactive explainer for the Mahler conjecture (openai/math family 087): drag shapes, watch their polar twins, see 3D Blender renders and the proof's lens trick.","id":"ph74gt3rfy7fdwqfp5rk2psabd8g553f","key":"mahler-shadow-twin","name":"Mahler: shadow twins","namespaceId":"k977fb4w5km4539pw9bsq3dm4d87ffwc","publishStatus":"private","updatedAt":1791769346819,"versions":[{"approvalStatus":"not_requested","createdAt":1791769356833,"htmlByteSize":1099170,"htmlSha256":"b9b00251123c1a11d588abb80898fb34de450c760162f883a1ae7e6939094fde","sdkVersion":"surface-sdk.v1","version":"0.1.0","sourceUrl":null},{"approvalStatus":"not_requested","createdAt":1791769913058,"htmlByteSize":1106006,"htmlSha256":"8e72660217b6565380aea2346430232d167587061648863d0095d1c2312f0030","sdkVersion":"surface-sdk.v1","version":"0.2.0","sourceUrl":null},{"approvalStatus":"not_requested","createdAt":1791770166522,"htmlByteSize":1107488,"htmlSha256":"e1d328dafca116a257665278e3f3f1d8500b2fd4280aba3976a04af49ab21982","sdkVersion":"surface-sdk.v1","version":"0.3.0","sourceUrl":null}],"canManage":true,"icon":null,"archivedAt":null},"version":"0.3.0","released":false,"previewUrl":"https://traces.com/s/52797cc3-0c4f-4505-b3fa-3bb171d53396?surface=mahler-shadow-twin&version=0.3.0","nextStep":"Ask the user to try previewUrl; when happy, call traces_surfaces_release_version with version \"0.3.0\" to make it current."}

"How the proof works" now folds up, and it starts closed, so the playground is right under the opener and the three cards. Version 0.3.0, still private and unreleased: https://traces.com/s/52797cc3-0c4f-4505-b3fa-3bb171d53396?surface=mahler-shadow-twin&version=0.3.0

When closed it shows as one row: a chevron, the title, and "5 moves · show" on the right. Clicking anywhere on the row opens it, and the label changes to "hide".
The fold also holds the three closing notes (other routes in the family, how the model got there, how sure this is), so the top stays compact. One trade-off: the "How sure is this?" verification caveat is now hidden until someone opens the fold.

The file builds and its scripts parse, but I haven't opened this version in a browser, so check the fold there.

Two changes you might want:

Start it open, so first-time readers see the argument by default and the fold is just for collapsing it.
Move "How sure is this?" out of the fold, so the caveat stays visible.

No content