Bolzano: From Expert-Guided Proof Search to Automated Open-Problem Solving
cs.AI, cs.CL
Submitted: 2026-10-07
Updated: 2026-10-09
Comments: Accepted at the 6th Workshop on Mathematical Reasoning and AI (MATH-AI), NeurIPS 2026
License: http://creativecommons.org/licenses/by-sa/4.0/
The gist: Large language models are increasingly contributing to mathematical research, where progress often depends on efficient proof search, incremental improvements and careful verification.
Abstract
Large language models are increasingly contributing to mathematical research, where progress often depends on efficient proof search, incremental improvements and careful verification. We describe Bolzano, a multi-agent open-source system that uses parallel prover agents with a verifier agent and maintains a human-readable research state. Initial manual use on expert-selected problems yielded 8 results whose proofs were checked by domain experts. Motivated by these case studies, we ran Bolzano without problem-specific human guidance on about 3,800 open problems extracted from four sets of papers, solving about 200 open problems. One experiment used papers accepted to STOC 2026, a top conference in theoretical computer science. There, we answered four questions raised in the papers, as confirmed by their authors.
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- Bolzano: Case Studies in LLM-Assisted Mathematical Research
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- Semi-Autonomous Mathematics Discovery with Gemini: A Case Study on the Erd\H{o}s Problems
- A Unified Approach to Memory-Sample Tradeoffs for Detecting Planted Structures
- AlphaEvolve: A coding agent for scientific and algorithmic discovery
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