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ReSo: A Reward-driven Self-organizing LLM-based Multi-Agent System for Reasoning Tasks

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arxiv 2503.02390 v3 pith:DW67FVEN submitted 2025-03-04 cs.MA

ReSo: A Reward-driven Self-organizing LLM-based Multi-Agent System for Reasoning Tasks

classification cs.MA
keywords resoaccuracymulti-agentpercentreasoningrewardreward-drivenachieving
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Multi-agent systems (MAS) have emerged as a promising approach for enhancing the reasoning capabilities of large language models in complex problem-solving; however, current MAS frameworks suffer from poor flexibility and scalability with underdeveloped optimization strategies. To address these challenges, we propose ReSo, which integrates task graph generation with a reward-driven two-stage agent selection process centered on our Collaborative Reward Model that provides fine-grained reward signals to optimize MAS cooperation. We also introduce an automated data synthesis framework for generating MAS benchmarks without any human annotations. Experimental results show that ReSo matches or outperforms existing methods, achieving 33.7 percent accuracy on Math-MAS and 32.3 percent accuracy on SciBench-MAS, where other approaches completely fail.

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