How mathagora works, and why it is built around the needs of mathematical research.
mathagora is a public working layer for mathematics: a place between private AI conversations and finished papers where mathematical problems, AI-assisted investigations, partial progress, and failed attempts are shared and kept reusable.
It works like a question-and-answer site for research-level mathematics, combined with a record of how each piece of work was produced: by whom, with which tools and AI systems, and building on what.
An Agentic Task describes what should be done with one or more Mathematical Items — for example, prove it, disprove it, repair it, explain it, formalize it, search the literature for it, simplify a proof, generalize it, find a counterexample, or check whether two formulations are equivalent.
It is the unit people work on and answer: it names the items involved, the kind of task, and an optional prompt describing exactly what is being asked.
A Mathematical Item is a piece of mathematical content: a conjecture, theorem, lemma, definition, proof, argument, construction, question, counterexample, dataset, mathematical object, computation, or another mathematical artifact.
An item does not have to be correct to be recorded. Items are versioned, so work can always point to the exact formulation it investigated.
The Mathematical Item is the mathematics being referred to. The Agentic Task is a particular piece of work applied to it.
Keeping them separate means the mathematical content is stored once, and every task and answer refers back to the same item (and the exact version of it) instead of each Agentic Task carrying its own copy of the statement.
Because in mathematics the same statement invites very different work. One conjecture can have one task asking for a proof, another asking for a counterexample, another asking whether it is equivalent to a different formulation, and another asking for a literature search.
Each of these is a different prompt for the same mathematics, with its own answers. The item's page lists all Agentic Tasks that use it.
Related mathematics is often recorded as separate items: a generalization, a special case, an equivalent reformulation, a closely related problem. When you create a new item, mathagora suggests existing items with similar titles or content, so you can either use an existing item instead of creating a duplicate, or create your item and connect it to one or more of them.
Connections are simple, two-way links between items. On an Agentic Task's page, the “Connected Agentic Tasks” list shows tasks on items directly connected to that task's items, so related work is easy to find.
Prepare a JSON transcript following our conversation-export schema. Upload it directly, or bundle it with supporting files in a ZIP. To help create the JSON, we provide an export prompt in the Answer Package upload form that you can copy and paste into your work session. Depending on your tool and setup, it may or may not capture the session accurately. You can adapt the prompt or use your own export tools.
Keep the original records and compare the export with your session: messages, notation, code, and attachments. Remove secrets and unrelated private material. Fix formatting only where the original supports the correction; never invent missing dialogue or replace the opening prompt to pass validation.
Exports and context compression can lose earlier material. Describe gaps and substantive corrections in completeness.details. A complete: false value can mean uncertainty, not necessarily missing messages, and does not block submission. The reader's Claimed complete badge reflects the exporter's declaration, not verification by Mathagora.
Standalone JSON must match the schema and the Agentic Task's opening prompt. If the opening prompt is unavailable, share surviving records in a ZIP and explain the gaps. Every ZIP needs an index.json at its root listing the transcripts and starting conversations; the upload form includes an example. Your browser checks the index and referenced files before upload. ZIP contents are not checked for completeness. Acceptance does not certify completeness, fidelity, or mathematical correctness.
A baseline is an ordinary Answer Package tagged Baseline. It records an initial attempt so readers and later contributors can see what has already been tried.
For new submissions, the tag is added automatically when a JSON or ZIP contains exactly one readable conversation: the original Item and task prompt, an assistant reply, and no further human turn. Multiple conversations or follow-ups do not qualify. The tag is ordinary community-editable metadata, not verification of the export.
Baselines are optional and can be submitted only after an Agentic Task is published, with the same transcript upload and validation as other Answer Packages. mathagora does not run the model itself; a person submits the attempt. There is no separate failure-description requirement.
Different people try different models, prompts, and versions, and new attempts keep becoming possible over time. Any signed-in user can submit an Answer Package tagged Baseline to a published task, keeping that record growing instead of freezing it at the original author's attempt.
Each baseline shows its submitting person and uses ordinary Answer Package permissions and version history. Its author or a moderator can revise it; deletion and restoration follow Answer Package moderation rules.
Answers are submitted as Answer Packages, which can bundle an answer or explanation, proofs, counterexamples, computational results, formalization artifacts, references, files, and a development record describing how the work was produced.
A package can be full or partial. Partial progress is often valuable on its own, and a package can build on an earlier one, so work can accumulate step by step rather than only being recorded once a complete solution exists.
Bronze records are allowed to be incomplete. They capture how mathematical work actually developed and can bring together surviving conversations, a description of the strategy, workspace files, results, and an honest account of what is missing. Bronze is not a certification of a complete transcript, reproducibility, or a clean separation of human and AI contributions.
Bronze describes the development record, independently of whether the mathematical answer is full or partial and whether its contributions are human, AI, or human + AI. A useful Bronze package does not need to recreate the investigation from scratch.
Yes. A project may start casually and become serious later. Losing the original prompt or early threads does not, by itself, make the surviving work unsuitable for Bronze. Context compression can leave an agent unable to recover earlier messages; conversations can also depend on files rather than containing every line written to them. A chat alone may therefore not explain the work.
Share the latest surviving threads, strategy, and relevant workspace files or file versions that you can make public. Explain which messages, starting context, intermediate file versions, or other material are missing. Label a current workspace snapshot as current, not as evidence of its earlier contents. A retrospective summary is useful background, not a verbatim transcript: never invent missing dialogue or prompts.
For a structured transcript, report gaps in completeness.details and do not claim it is complete. Standalone JSON uploads still require a schema-valid transcript whose recorded opening prompt matches the linked Item and task prompt. If that prompt is unavailable, preserve the surviving records and supporting files in a ZIP and explain the gaps. ZIP contents are not checked for completeness, and acceptance does not verify completeness, fidelity, or correctness.
Gold: TBA. The intended direction is a deliberately structured record with clear separation of human and AI contributions. This could involve recreating earlier Bronze work under those conditions, or recording work that way from the beginning.
The exact requirements, format, and verification for Gold are not yet defined, and Gold packages are not supported. This is a future direction, not a current guarantee. You do not need a Gold-style reconstruction to share useful Bronze work.
Readers need to know how a piece of work came about in order to judge it and reuse it. Answer Packages are classified as human, AI, or human + AI, and that classification stays attached to the package as it was submitted.
The input side — Mathematical Items and Agentic Tasks — is human-responsible: AI may help formulate a Mathematical Item or task, but the person submitting it is expected to understand it and take responsibility for it — or to explain honestly where their understanding stops and why the problem still seems meaningful. Answers, on the other hand, may be fully AI-generated, as long as that is recorded.
Its structure follows how mathematical work is actually done and checked:
Tags make work findable in the terms mathematicians already use. There are three kinds, each shown in its own color: mathematical-area tags (based on the math.stackexchange tag set, such as general-topology or number-theory), formalization tags for proof assistants and formal languages (such as Lean, Isabelle, Rocq, or a “Custom Formal Language” tag for a language you define yourself), and other tags such as “code environment” and “for the record”.
“for the record” marks tasks that are not new research but are worth recording, for example to document how current AI systems handle a problem. Anyone signed in can add existing tags and, for now, create new ones by choosing their type.
mathagora records and organizes mathematical work; it does not produce it. The platform does not solve problems, generate Answer Packages, formalize mathematics, or judge correctness automatically — those are done by people and by the AI systems they choose to use, and the results are submitted with their provenance.
This keeps the platform model-agnostic and keeps responsibility and evaluation with people. For convenience, an Agentic Task can be copied in a machine-readable format or opened in an external AI tool, but the attempt itself happens outside the platform.
A “StackOverflow for agents” is a general question-and-answer site where AI agents post and answer questions. mathagora is built around the specific requirements of mathematics instead: