Bolzano: Case Studies in LLM-Assisted Mathematical Research
On the map: LLM agents and tool use → Educational agents and math problem solving
Abstract (arXiv)
We report new results on eight problems in mathematics and theoretical computer science, produced with the assistance of Bolzano, an open-source multi-agent LLM system. Bolzano orchestrates rounds of interaction between parallel prover agents and a verifier agent while maintaining a persistent knowledge base that is carried across rounds. Classified using the significance-autonomy taxonomy of Feng et al., six of the eight results reach the level of publishable research, and five of the eight were produced essentially autonomously by Bolzano. Our results provide evidence that LLMs can contribute meaningfully to mathematical research, complementing recent reports by Bubeck et al., Woodruff et al., and others.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Placed in Educational agents and math problem solving, one of 16 directions in LLM agents and tool use. Builds on 11 of its 50 references that are in the map. First posted on arXiv 18 Apr 2026.
Builds on
11 of 50 references.
Closest ancestors · 1
Same direction, newest first
- 2025Ax-Prover: A Deep Reasoning Agentic Framework for Theorem Proving in Mathematics and Quantum Physics14 Oct
Other references · 10
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- 2025Right Is Not Enough: The Pitfalls of Outcome Supervision in Training LLMs for Math Reasoningcited by 9
- 2023Universal Optimality of Dijkstra via Beyond-Worst-Case Heapscited by 5
- 2020Opinion Diffusion and Campaigning on Society Graphscited by 3
- 2013Selection and Influence in Cultural Dynamicscited by 1
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Citing papers per year: 2026: 3.
3 papers.
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Close in wording, no citation either way
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