arXiv AI
Jun 9
TheoremBench: Evaluating LLMs on Theorem Proving in Formal Mathematics
arXiv:2606. 09450v1 Announce Type: new Abstract: LLMs have recently achieved strong results on formal proving benchmarks.
IneqAgent: Agentic Inequality Proving in Lean with Symbolic Computation
IneqAgent is a new framework that combines large language model reasoning with symbolic computation to prove inequalities in Lean4. It uses lemma-level Monte Carlo Tree Search to coordinate lemma construction, goal reformulation, and backend selection, employing sum‑of‑squares, Bernstein, and successive difference‑substitution backends to generate exact certificates. In tests on four benchmarks, IneqAgent with DeepSeek‑V4‑Flash solved 175 of 186 problems (94.09%) and produced Lean‑verified strengthenings of Dittert’s conjecture and a certification of the Holens–Djokovic $(4,4)$ inequality.
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Hugging Face Trending Papers
Jun 8
TheoremBench: Evaluating LLMs on Theorem Proving in Formal Mathematics
LLMs have recently achieved strong results on formal proving benchmarks. However, existing evaluations remain heavily concentrated on competition-style problems and often fail to capture how models behave on longer, more dependency-rich mathematical developments.
arXiv AI
Jun 3
LEAP: Supercharging LLMs for Formal Mathematics with Agentic Frameworks
arXiv:2606. 03303v1 Announce Type: new Abstract: Large Language Models (LLMs) exhibit strong informal mathematical reasoning but struggle to generate mechanically verifiable proofs in formal languages like Lean.
arXiv AI
Jun 16
SorryDB: Can AI Provers Complete Real-World Lean Theorems?
arXiv:2603. 02668v2 Announce Type: replace Abstract: We present SorryDB, a dynamically-updating benchmark of open Lean tasks drawn from 78 real world formalization projects on GitHub.
arXiv AI
Jun 6
Goedel-Architect: Streamlining Formal Theorem Proving with Blueprint Generation and Refinement
arXiv:2606. 06468v1 Announce Type: new Abstract: We introduce Goedel-Architect, an agentic framework for formal theorem proving in Lean 4 centered on blueprint generation and refinement.
arXiv AI
Oct 9
LEVER: Adaptive Cost-Aware Proof Search Over AND/OR Graphs
LEVER is a new proof‑search algorithm that lets users program the objective over correct proofs and optimizes it during search. It scores partial proofs on an AND/OR graph, combining achieved objective values with predictions for open subgoals, so the objective guides search before a proof is complete. On the PutnamBench in Lean 4, LEVER reduces computational cost by 34 % and raises the solve rate from 80 % to 96 %, while also improving topical impurity and approaching refactoring for proof length.
arXiv Machine Learning
5d ago
h1: A Taste of the Bitter Lesson in Automated Theorem Proving
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.
arXiv AI
Jul 1
Beyond the Library: An Agentic Framework for Autoformalizing Research Mathematics
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.
arXiv AI
Jun 12
Pythagoras-Prover: Advancing Efficient Formal Proving via Augmented Lean Formalisation
arXiv:2606. 12594v1 Announce Type: new Abstract: Modern Lean theorem provers achieve strong performance only with substantial training and inference compute, driven in part by scarce verified proof data and the long reasoning traces of formal proof search, making both supervised fine-tuning (SFT) and sampling expensive.
arXiv AI
Jun 9
Advancing Mathematics Research with AI-Driven Formal Proof Search
arXiv:2605. 22763v2 Announce Type: replace Abstract: Large language models (LLMs) increasingly excel at mathematical reasoning, but their unreliability limits their utility in mathematics research.
arXiv AI
Jun 2
Formally Solving Answer-Construction Problems in Lean
arXiv:2505. 18492v5 Announce Type: replace Abstract: Mathematical competition problems fall into two broad types: theorem proving, which asks for a proof of a given statement, and answer construction, which requires constructing a property-satifying object with proofs.
arXiv AI
Oct 1
Growing an Agent/Prover Interface: Evolutionary Tool Design for Cost-Efficient Theorem Proving in Rocq and Lean
arXiv:2609.39544v1 Announce Type: new Abstract: Recent achievements in AI-assisted mathematics require intensive interaction of agents with proof assistants to generate machine-checked proof certific...