RMA: an Agentic System for Research-Level Mathematical Problems
The article introduces Research Math Agents (RMA), a framework designed for automated reasoning on complex mathematical problems. RMA employs a multi-agent system to enhance problem-solving through structured reasoning and iterative feedback. The framework has demonstrated superior performance on the First Proof benchmark, solving eight out of ten research problems more effectively than existing models.
- ▪RMA targets research-level mathematical problems requiring long-horizon reasoning and iterative proof refinement.
- ▪The framework decomposes proof solving into specialized modules coordinated by initializer, proposer, and verifier agents.
- ▪RMA outperformed strong baselines, including GPT-5.2R, in solving research problems and producing sound proofs.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.22875 |
| Publication time | Mon, 25 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-25T04:07:35.648Z |
| Last seen | 2026-05-25T04:07:35.648Z |
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| Summary source text | contentText |
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| Commercial reuse | May the content be reused commercially? | Not permitted |
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Computer Science > Artificial Intelligence arXiv:2605.22875 (cs) [Submitted on 20 May 2026] Title:RMA: an Agentic System for Research-Level Mathematical Problems Authors:Zelin Zhao, Bo Yuan, Jaemoo Choi, Yongxin Chen View a PDF of the paper titled RMA: an Agentic System for Research-Level Mathematical Problems, by Zelin Zhao and 3 other authors View PDF Abstract:We present $\textbf{Research Math Agents (RMA)}$, an agentic framework for automated reasoning on research-level mathematical problems. Unlike prior studies centered on competition mathematics or formal theorem proving, RMA targets research-level mathematical problems that require long-horizon reasoning, literature grounding, and iterative proof refinement.
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