arXiv AI

Bolzano: From Expert-Guided Proof Search to Automated Open-Problem Solving

Bolzano is a multi‑agent, open‑source system that employs parallel prover agents and a verifier agent to conduct proof search while maintaining a human‑readable research state. In initial manual trials on expert‑selected problems, it produced eight proofs verified by domain experts. When deployed without problem‑specific guidance on roughly 3,800 open problems from four paper sets, Bolzano solved about 200 of them, including four questions from papers accepted to the STOC 2026 conference, which were confirmed by the authors.

arXiv Computation and Language
Oct 8

From Expert-Guided Proof Search to Automated Open-Problem Solving

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 AI
Oct 1

Cogentic: Multi-Agent Orchestration for Automated Proof Discovery

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 AI
4d ago

OpenFrontiers: A Collection of Open Research Problems and a Benchmark for Scientific Rediscovery

OpenFrontiers is a cross‑disciplinary resource that compiles 12,151 open research problems and a benchmark of 200 recently resolved mathematics problems, including 110 solved in 2026 and 90 still open. Each benchmark problem is formally stated in Lean 4, verified by automated probes, and reviewed independently. The evaluation protocol tests LLM agents under closed‑book, frozen‑retrieval, and live‑web conditions, revealing that closed‑book rediscovery is rare while live‑web success largely reflects retrieval of published solutions.

By Jiarui Yao, Ruida Wang, Tong Zhang
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
arXiv Machine Learning
Sep 11

Measuring Progress in Reasoning Toward Mathematical Discovery with Automatic Verification

The paper introduces HorizonMath, a benchmark of 113 largely unsolved mathematical problems across eight domains, paired with an open-source framework for automated verification. It focuses on the generator‑verifier gap, targeting problems that are hard to discover but easy to verify computationally, thereby avoiding costly formal proof verification or manual review. Using this framework, the authors found six novel solutions—three each from GPT‑5.4 Pro and GPT‑5.6 Sol—demonstrating that current models can contribute to mathematical research, while most state‑of‑the‑art models score below 10%.

By Erik Y. Wang, Sumeet R. Motwani, James V. Roggeveen, Eliot Hodges, Dulhan Jayalath, Charles London, Kalyan Ramakrishnan, Jakob Foerster, Cheng Zhang, Flaviu Cipcigan, Philip Torr, Alessandro Abate
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.

By Austin Letson, Leopoldo Sarra, Auguste Poiroux, Oliver Dressler, Paul Lezeau, Dhyan Aranha, Frederick Pu, Aaron Hill, Miguel Corredera Hidalgo, Julian Berman, George Tsoukalas, Lenny Taelman