arXiv:2610.09769v1 Announce Type: cross
Abstract: Large language models are increasingly contributing to mathematical research, where progress often depends on efficient proof search, incremental imp...
By Adri\'an Z\'ame\v{c}n\'ik, Mat\v{e}j Kripner, Martin Kouteck\'y, Martin Balko, Jan Greb\'ik, Pavel Hub\'a\v{c}ek, Robert \v{S}\'amal, V\'aclav Rozho\v{n}
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
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.
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.
By Chenyang An, Qihao Ye, Minghao Pan, Jiayaun Zhang
Cogentic is a multi‑agent system designed to automate proof discovery for open research problems. It uses an iterative prove‑verify loop where an orchestrator assigns independent provers to different proof directions, verifies their outputs with specialized components, and records confirmed intermediate results in a persistent ledger for future rounds. Built on Gemini, Cogentic has produced novel results on five open problems in online learning, auction theory, and mechanism design, each verified by domain experts and detailed in companion papers.
By Yang Cai, Vineet Gupta, Yanchen Jiang, Christopher Liaw, Aranyak Mehta, Grigoris Velegkas, Di Wang
arXiv:2608.28433v2 Announce Type: replace
Abstract: Proof assistants such as Lean 4 promise the paradigm of formally verified mathematics, but large-scale formalization projects have faced major barr...
By Shuze Chen, Kunal Marwaha, Xiaoyang Lu, Henry Yuen, Tianyi Peng