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Reducing Cognitive Overhead in Tool Use via Multi-Small-Agent Reinforcement Learning
Recent advances in multi-agent systems highlight the potential of specialized small agents that collaborate via division of labor. Existing tool-integrated reasoning systems, however, often follow a single-agent paradigm in which one large model interleaves long-horizon reasoning with precise tool o
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