AI RESEARCH
EvoMAS: Learning Execution-Time Workflows for Multi-Agent Systems
arXiv CS.AI
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ArXi:2605.08769v1 Announce Type: new Large language model (LLM)-based multi-agent systems have shown strong potential on complex tasks through agent specialization, tool use, and collaborative reasoning. However, most automated multi-agent system design methods still follow a one-shot paradigm: a workflow is optimized or selected before execution and then reused unchanged throughout the task. This static coordination strategy is ill-suited for long-horizon tasks whose subgoals, intermediate evidence, and information needs evolve over multiple execution stages.