AI Summary: This paper adapts the classic 'MapReduce' concept from Big Data to the world of AI agents. It solves the problem of agents getting stuck on a single line of thought by forcing a swarm of agents to explore dozens of different ideas at once (the Map phase).
Traditional multi-agent systems often struggle with 'search breadth' in unstructured environments, leading to tunnel vision in reasoning. We propose A-MapReduce, a framework that applies the MapReduce paradigm to agentic reasoning. In the 'Map' phase, a fleet of independent agents explores diverse reasoning trajectories in parallel; in the 'Reduce' phase, a central aggregator synthesizes these paths into a single, verified conclusion. A-MapReduce demonstrates significant improvements in complex multi-step discovery tasks, such as legal research and chemical synthesis planning, by effectively managing massive search breadths without a corresponding increase in latency.
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