arXiv:2607. 08986v2 Announce Type: replace Abstract: We formalize a research result in the Lean 4 proof assistant by having a mathematician direct an AI system, and frame the activity as a formalization game.
By Joseph K. Miller
arXiv:2606. 31134v1 Announce Type: new Abstract: While Large Language Models (LLMs) have demonstrated exceptional capabilities in mathematical reasoning, they frequently produce subtle errors that evade human detection.
By Arshia Soltani Moakhar, Iman Gholami, Max Springer, Mahdi JafariRaviz, MohammadTaghi Hajiaghayi
arXiv:2610.08144v1 Announce Type: cross
Abstract: Autoformalisation is increasingly used to verify mathematical texts, including those generated by AI, as in OpenAI's announced proof of blow-up of so...
By Alexander Bastounis, Fabian Circelli, Anders C. Hansen
The paper introduces h1, a simple agentic loop that interfaces a language model with Lean 4, avoiding complex human-designed workflows. h1 achieves state‑of‑the‑art performance on benchmarks such as PutnamBench, FATE‑X, and FormalConjectures100‑Solved, solving eight open problems and matching or surpassing specialized systems like Goedel‑Architect and AlphaProof Nexus. Experiments show that an efficient Lean interface is crucial for cost and success, suggesting that future evaluations should prioritize execution feedback over elaborate workflows.
By Skander Moalla, Ahmad Rammal, Niket Patel, Remi Munos, Julia Kempe
arXiv:2606. 28572v1 Announce Type: cross Abstract: The axiom of choice has divided the foundations of mathematics for over a century, but the distinction between classical and constructive proofs has remained a philosophical and methodological one.
By Rodrigo Mendoza-Smith
arXiv:2606. 05400v1 Announce Type: cross Abstract: Long-horizon autoformalization of research mathematics fails not only at hard lemmas, but at scale: statements drift, dependencies tangle, context decays, and local repairs corrupt distant work.
By Yuanhe Zhang, Yuekai Sun, Taiji Suzuki, Jason D. Lee, Fanghui Liu