The Big Picture
Splitting hard proofs into many tiny, verifiable tasks and confining human review to a few curated milestones lets small teams of AI agents produce large, reusable machine-checked mathematics quickly.
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The Evidence
An open platform lets AI agents and human auditors collaborate: theorems are stored as immutable statements and multiple agents can submit independent proofs. proof-sketches break hard proofs into atomized sub-problems so agents build on one another without editing the same files. Audited missions keep human effort focused on a small set of canonical statements, while the rest of the work is automated and becomes a reusable library. immutable statements
Data Highlights
1Largest mission reached ~151,000 lines of formalization (comparable to a 130,000-line centralized project)
2That largest mission was closed by 6 agents running on two consumer subscriptions versus ~30,000 agent runs on a metered API pipeline reported elsewhere
3A smaller mission produced 17,000 lines in one week using a single subscription with four subagents
What This Means
Engineers building or integrating AI agents can use the platform to scale proof generation without central orchestration. Technical leaders can evaluate how to reduce human review effort by focusing auditors on mission milestones. Researchers interested in multi-agent workflows and trust mechanisms can test coordination, reuse, and reputation ideas at scale.
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Key Figures
Fig 3: Figure 3: A read-back of a drafted statement. The auditor agent sees only the Lean code (top) and renders what it literally asserts (bottom), unfolding the two problem-specific definitions and accounting for every binder and hypothesis, including those a source statement would leave implicit: that the two finite sets are quantified independently, that an empty product is 1 1 , and that the hypothesis is only n < M n<M . The human auditor compares this testimony against the source statement.
Fig 5: Figure 5: Decomposition graph for the Sensitivity Conjecture proved in Huang (2019) . Each dependent theorem node represents a child lemma, with an extra assisting lemma showing that A A corresponds to the adjacency matrix. They are connected by a proof-sketch (purple box).
Fig 6: Figure 6: The milestone list for the Sensitivity Conjecture mission. Each milestone pairs an authoritative statement, transcribed from the source proof, with the platform theorem the captain has attested as its canonical formalization. The two milestones shown are precisely Cauchy’s interlace theorem and the spectrum of A n A_{n} from Section 4.2 , here linked to the proved theorems cauchy_interlacing_sorted and huang_matrix_spectrum_sorted . The header tracks how many milestones have been reached and how many of the mission’s theorems are proved.
Fig 7: Figure 7: A snapshot of discussion under the exact matrix completion mission. Two different agents are sharing their latest progress.
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Learn MoreConsiderations
The case studies are illustrative, not controlled experiments, so gains may mix stronger models with better coordination tools. Human judgment still decides what to formalize, writes mission milestones, and audits canonical statements. Open submissions invite low-quality or adversarial content, so reputation, filtering, and robust auditing are required as the platform grows.
Methodology & More
Prove2Me is a collaborative platform that treats each theorem as an immutable object and allows multiple proofs to be attached independently. By separating statements from proofs and introducing "proof-sketches," it decomposes difficult theorems into many small, independently solvable sub-problems. Agents work on these atomized tasks and import proved results as reusable building blocks; human auditors only verify a curated set of mission milestones to ensure the corpus stays faithful to the original sources.
Practical results show that small groups of agents can produce project-scale formalizations: one mission reached about 151K lines and closed with six agents on two consumer subscriptions, while another produced 17K lines in a week with a single subscription and four subagents. The platform uses pinned verification environments (the Lean proof assistant and a specific math library revision are examples) so every submission is reproducible. Remaining challenges include robust search across a growing corpus, handling adversarial or low-quality submissions, and extracting human-legible insight from machine-generated proofs — all active areas for future work and experimentation. reproducible
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Credibility Assessment:
Contains at least one recognizable researcher (Henry Yuen) and multiple authors, but affiliations and formal venue are not listed (arXiv only), so rated as a solid/recognized but not top-tier.