arXiv AI

QED: An Open-Source Multi-Agent System for Generating Mathematical Proofs on Open Problems

arXiv:2604. 24021v4 Announce Type: replace Abstract: We present QED, an open-source multi-agent system that turns human-provided research questions into complete mathematical proofs without further human guidance.

arXiv AI

Jul 13

ProofCouncil: An LLM Agent for Solving Open Mathematical Problems

arXiv:2607. 09474v1 Announce Type: new Abstract: Large language models (LLMs) have shown increasing promise in solving open problems in mathematics.

By Johannes Schmitt, Tim Gehrunger, Jasper Dekoninck, Gergely B\'erczi, Uri Kreitner, Liam Price, David Holmes

llms agents

Hugging Face Trending Papers

Jul 10

ProofCouncil: An LLM Agent for Solving Open Mathematical Problems

Large language models (LLMs) have shown increasing promise in solving open problems in mathematics. However, their performance can be further improved through agentic workflows tailored to real-world mathematical practice.

llms agents

arXiv AI

Jul 7

MechMath Agent Team: LLM Driven Agents for Mathematical Research

arXiv:2607. 04394v1 Announce Type: new Abstract: AI reasoning has become a central focus in contemporary artificial intelligence, largely driven by the success of large language models.

By Yichuan Cao, Ruichen Qiu, Junqi Liu, Jiaqi Wang, Dakai Guo, Ruyong Feng, Lihong Zhi, Xiao-Shan Gao

llms rag agents

Hugging Face Trending Papers

Jul 5

MechMath Agent Team: LLM Driven Agents for Mathematical Research

AI reasoning has become a central focus in contemporary artificial intelligence, largely driven by the success of large language models. However, mathematical research, which is characterized by non-linear derivation paths, rigorous logical requirements, and protracted exploration cycles, poses severe challenges for existing reasoning systems.

llms rag agents

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.

By George Tsoukalas, Anton Kovsharov, Sergey Shirobokov, Anja Surina, Moritz Firsching, Gergely B\'erczi, Francisco J. R. Ruiz, Arun Suggala, Adam Zsolt Wagner, Eric Wieser, Lei Yu, Aja Huang, Mikl\'os Z. Horv\'ath, Andrew Ferraiuolo, Henryk Michalewski, Edward Lockhart, Codrut Grosu, Thomas Hubert, Matej Balog, Pushmeet Kohli, Swarat Chaudhuri

llms agents

arXiv AI

Jun 2

Automated Conjecture Resolution with Formal Verification

arXiv:2604. 03789v2 Announce Type: replace-cross Abstract: Recent advances in large language models have significantly improved their ability to perform mathematical reasoning, extending from elementary problem solving to increasingly capable performance on research-level problems.

By Haocheng Ju, Guoxiong Gao, Jiedong Jiang, Bin Wu, Zeming Sun, Shurui Liu, Leheng Chen, Yutong Wang, Yuefeng Wang, Zichen Wang, Wanyi He, Peihao Wu, Liang Xiao, Ruochuan Liu, Bryan Dai, Bin Dong

llms agents

arXiv AI

Jul 10

From Solvers to Research: Large Language Model-Driven Formal Mathematics at the Research Frontier

arXiv:2607. 07779v1 Announce Type: cross Abstract: Recent developments in AI for Mathematics (AI4Math), especially Large Language Model (LLM)-driven theorem provers, has achieved remarkable success in formal proof generation for well-defined mathematical problems through Interactive Theorem Proving (ITP) languages.

By Eric Jiang, Xiao Liang, Yikai Zhang, Yingjia Wan, Mengting Li, Haikang Deng, Alexander K. Taylor, Justin Baker, Rushil Raghavan, Junyi Zhang, Ying Nian Wu, Andrea L. Bertozzi, Kai-Wei Chang, Raghu Meka, Matthew Sottile, Nanyun Peng, Amit Sahai, Terence Tao, Wei Wang

llms agents

arXiv AI

Jul 13

OpenProver: Agentic and Interactive Theorem Proving with Lean 4

arXiv:2607. 09217v1 Announce Type: new Abstract: In this system paper, we present OpenProver, an open-source system for LLM-driven automated theorem proving (ATP) with integrated Lean 4 formal verification.

By Mat\v{e}j Kripner, Milan Straka

llms agents benchmarks

arXiv AI

Jul 17

MathCoPilot: An Interactive System for Human-AI Symbiotic Paradigm of Mathematical Research

arXiv:2607. 14582v1 Announce Type: new Abstract: Existing LLM-based theorem provers have achieved impressive results on formal mathematics benchmarks, yet they remain confined to acting as autonomous agents that prove a stated proposition.

By Junjie Zhang, Jiayu Liu, Wenbin Liu, Zhenya Huang, Doudou Wang, Yan Jiang, Leiye Xu, Tao Xiong, Wen Huang, Qi Liu, Guoping Hu, Enhong Chen, Mengping Zhang, Xiangdong Ye

llms rag agents benchmarks

Hugging Face Trending Papers

Jul 30

MECA: A Mechanism-Centered Agent for Constructing Well-Specified and Valuable Mathematical Conjectures

Automatically constructing well-specified and valuable mathematical conjectures remains a central challenge in AI-assisted mathematical discovery. Many existing open problems and conjectures are often too broad, underspecified, or difficult to connect to plausible proof or refutation strategies.

agents

arXiv AI

Jul 8

Danus: Orchestrating Mathematical Reasoning Agents with Fact-Graph Memory

arXiv:2607. 06447v1 Announce Type: new Abstract: Recent LLM-based mathematical reasoning agents have begun to tackle research-level problems and, in several cases, have contributed to the resolution of open problems.

By Jihao Liu, Guoxiong Gao, Zeming Sun, Bin Wu, Shurui Liu, Jiedong Jiang, Haocheng Ju, Leheng Chen, Ronnie Cheng, Xiping Zhang, Bin Dong

llms agents

arXiv AI

Jul 9

Evaluating SageMath-Augmented LLM Agents for Computational and Experimental Mathematics

arXiv:2607. 06820v1 Announce Type: new Abstract: Recent advances in AI for Mathematics have focused largely on autoformalization and theorem proving, leaving the role of Computer Algebra Systems (CAS) in agentic LLM workflows underexplored.

By Pavel Snopov, German Magai

llms agents benchmarks

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