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ProofEvolve is a neuro-symbolic framework that evolves formally verified proof structures using neural models to enhance automated theorem proving, achieving high solve rates on Lean benchmarks by preserving verified knowledge from incomplete attempts.
The paper introduces FAR, a human-AI discovery paradigm that automates the search for mathematical problems from literature, with a pilot in combinatorics demonstrating its effectiveness in identifying conjectures and resolutions.
An AI system called Theo Conjecture, leveraging a large language model, solved a 35-year-old graph theory problem originally posed by mathematician Paul Erdős, discovering an unexpected term. The system works by proposing, testing, and revising mathematical ideas in a loop.
A research paper introducing a failure-triggered cascade approach to safely integrate machine learning into Lean 4's grind tactic, achieving improved efficiency and solving previously unsolvable proofs without regressions.
OpenProver is an open-source system for LLM-driven automated theorem proving using Lean 4, featuring a Planner-Worker-Verifier architecture and both autonomous and interactive modes. It enables reproducible evaluation and human-AI synergy in mathematical proof search.
Research Math Agents (RMA) is an agentic framework for automated reasoning on research-level mathematical problems, achieving state-of-the-art results on the First Proof benchmark by solving 8 out of 10 problems, outperforming strong baselines like GPT-5.2R and Aletheia.
This paper introduces Formal Conjectures, an evolving benchmark of 2615 mathematical statements formalized in Lean 4, including open research conjectures for proof discovery and solved problems for auto-formalization, designed to evaluate automated reasoning systems with zero contamination.