DynaFlow：学习自由形式多智能体编排
DynaFlow: Learning Free-Form Multi-Agent Orchestration
DynaFlow 通过让主智能体合成可执行的流程来协调子智能体，实现自适应多智能体协作。该框架在主智能体的动作空间中集成了自由形式的可执行流程合成，主智能体决定何时需要协作并生成特定任务的程序，通过顺序或并行执行、结构化信息交换和验证来协调子智能体。
Published on Oct 25
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Abstract
DynaFlow enables adaptive multi-agent collaboration by letting a main agent synthesize executable workflows that coordinate sub-agents through structured execution and verification.
Multi-agent systems often rely on predefined collaboration structures, limiting their ability to adapt to diverse task requirements. We introduce DynaFlow, a framework that integrates free-form executable workflow synthesis into the action space of the main agent. The agent decides when collaboration is needed and generates task-specific programs that coordinate sub-agents through sequential or parallel execution, structured information exchange, and verification. Execution results and traces inform subsequent actions, enabling the agent to adapt collaboration as reasoning progresses. We train only the main agent through supervised fine-tuning on distilled trajectories, followed by refinement on examples filtered for unsupported premises, biased verification, and information-flow defects. All sub-agents remain fixed, separating orchestration learning from executor capability improvement. Across six benchmarks spanning mathematical reasoning, scientific question answering, and multi-hop retrieval, DynaFlow achieves 88.08% overall accuracy in Forced mode, outperforming the strongest evaluated baseline by 10.90 percentage points with the same Qwen3.5-27B backbone. Workflow analysis finds more explicit use, verification, and refinement of upstream outputs after training, highlighting cross-stage information flow as a key dimension of multi-agent orchestration.
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来源: Hugging Face Daily Papers · huggingface.co