# AI for Mathematics & Formal Proof: State of the Field (October 2026)

Research dossier · v1 · updated 2026-10-30 · 3,259 words · 35 sources · 29 claims · verification: verified

This dossier surveys AI systems that prove theorems, translate informal mathematics into machine-checkable form and evaluate mathematical reasoning, as of October 2026. It covers neural provers for Lean and other proof assistants, olympiad-level results, benchmarks and open problems. The main takeaway is that a machine-checked proof is only as trustworthy as the formal statement it proves, which makes statement fidelity the central bottleneck.

## Contents

- Executive summary
- Background and core concepts
- Timeline of key milestones
- State of the art
  - Neural provers and expert iteration
  - Autoformalization and statement fidelity
  - Olympiad-level and competition systems
  - Agentic and harness-based provers
  - Quantitative benchmarks
- Open problems and active debates
- Major programs and players
- Outlook: what to watch (November 2026 to April 2028)
- Key papers
- Glossary

## Sample findings

- DeepSeek-Prover-V2 reports an 88.9% pass rate on miniF2F-test; its abstract does not state the sampling budget.
- DeepSeek-Prover-V2 reports 49 of 658 PutnamBench problems solved.

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