RMA: an Agentic System for Research-Level Mathematical Problems

📰 ArXiv cs.AI

Learn how RMA, an agentic system, tackles research-level mathematical problems with automated reasoning and long-horizon planning

advanced Published 25 May 2026

Action Steps

Build a modular architecture for research-level mathematical problem solving using RMA's framework
Configure problem analysis and literature grounding modules to handle complex mathematical problems
Apply iterative proof refinement techniques to improve solution accuracy
Test RMA's performance on various research-level mathematical problems
Compare RMA's results with human mathematicians' solutions to evaluate its effectiveness

Who Needs to Know This

Researchers and developers in AI and mathematics can benefit from RMA's capabilities in automated reasoning and proof refinement, enhancing their collaboration and research efficiency

Key Insight

💡 RMA's modular architecture and long-horizon planning enable it to tackle complex mathematical problems that require iterative proof refinement and literature grounding

Key Takeaways

Learn how RMA, an agentic system, tackles research-level mathematical problems with automated reasoning and long-horizon planning

Full Article

Title: RMA: an Agentic System for Research-Level Mathematical Problems

Abstract:
arXiv:2605.22875v1 Announce Type: new 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. RMA decomposes research-level proof solving into specialized modules for problem analysis, l

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