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About this paper
Yiqun Chen, Lingyong Yan, Zixuan Yang, Erhan Zhang, Jiashu Zhao, Shuaiqiang Wang, Dawei Yin, Jiaxin Mao
The M-ASK framework introduces a multi-agent system for agentic search that decouples search behavior from knowledge management, mitigating issues of context bloat and sparse rewards in monolithic designs. This approach achieves superior performance, particularly on multi-hop QA tasks with an average F1 score of 50.09, and demonstrates exceptional training stability with no observed model collapse.
Key insights
- Monolithic agent architectures suffer from fundamental issues like a long-horizon credit assignment problem, context bloating, and noise accumulation, leading to training instability and model collapse.
- Decoupling complex search tasks into specialized agent roles with active knowledge management significantly enhances stability and performance.
- Turn-level dense rewards are critical for effective local cooperation and accurate credit assignment in multi-turn, long-horizon tasks, outperforming sparse global feedback.
Summary by alphaXiv.
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