AI RESEARCH
M2A: Synergizing Mathematical and Agentic Reasoning in Large Language Models
arXiv CS.AI
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ArXi:2605.09879v1 Announce Type: new While reasoning has become a central capability of large language models (LLMs), the reasoning patterns required for different scenarios are often misaligned. Mathematical reasoning typically relies on intrinsic logic to solve closed-world problems in a single response, whereas agentic reasoning requires not only internal reasoning but also multi-turn interaction with external environments, interleaving thought and action.