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Advancing Mathematics Research with AI-Driven Formal Proof Search

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6 Pith papers citing it
abstract

Large language models (LLMs) increasingly excel at mathematical reasoning, but their unreliability limits their utility in mathematics research. A mitigation is using LLMs to generate formal proofs in languages like Lean. We perform the first large-scale evaluation of this method's ability to solve open problems. Our most capable agent autonomously resolved 9 of 353 open Erd\H{o}s problems at the per-problem cost of a few hundred dollars, proved 44/492 OEIS conjectures, and is being deployed in combinatorics, optimization, graph theory, algebraic geometry, and quantum optics research. A basic agent alternating LLM-based generation with Lean-based verification replicated the Erd\H{o}s successes but proved costlier on the hardest problems. These findings demonstrate the power of AI-aided formal proof search and shed light on the agent designs that enable it.

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2026 6

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UNVERDICTED 6

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Recursions for Mock Theta Functions

math.NT · 2026-06-16 · unverdicted · novelty 5.0

Derives weighted recursions for coefficients of mock theta functions f and ω via holomorphic projection on vector-valued Rankin-Cohen brackets, exploiting vanishing cusp form spaces.

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