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AI co-mathematician: Accelerating mathematicians with agentic AI

Researchers introduce the AI co-mathematician, a workbench for mathematicians to interactively leverage AI agents in open-ended research. It supports ideation, literature search, computational exploration, theorem proving, and theory building via an asynchronous, stateful workspace. Early tests helped solve open problems, identify new directions, and uncover overlooked literature. It achieves 48% on FrontierMath Tier 4, a new high score.

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[2605.06651] AI co-mathematician: Accelerating mathematicians with agentic AI

[Submitted on 7 May 2026 (v1), last revised 13 May 2026 (this version, v2)]

Title:AI co-mathematician: Accelerating mathematicians with agentic AI

View a PDF of the paper titled AI co-mathematician: Accelerating mathematicians with agentic AI, by Daniel Zheng and 17 other authors

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Abstract:We introduce the AI co-mathematician, a workbench for mathematicians to interactively leverage AI agents to pursue open-ended research. The AI co-mathematician is optimized to provide holistic support for the exploratory and iterative reality of mathematical workflows, including ideation, literature search, computational exploration, theorem proving and theory building. By providing an asynchronous, stateful workspace that manages uncertainty, refines user intent, tracks failed hypotheses, and outputs native mathematical artifacts, the system mirrors human collaborative workflows. In early tests, the AI co-mathematician helped researchers solve open problems, identify new research directions, and uncover overlooked literature references. Besides demonstrating a highly interactive paradigm for AI-assisted mathematical discovery, the AI co-mathematician also achieves state of the art results on hard problem-solving benchmarks, including scoring 48% on FrontierMath Tier 4, a new high score among all AI systems evaluated.

Comments: 23 pages; several citations added

Subjects:

Artificial Intelligence (cs.AI)

Cite as: arXiv:2605.06651 [cs.AI]

(or arXiv:2605.06651v2 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2605.06651

arXiv-issued DOI via DataCite

Submission history

From: Daniel Roy [view email] [v1] Thu, 7 May 2026 17:56:32 UTC (667 KB)

[v2] Wed, 13 May 2026 16:47:04 UTC (673 KB)

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