DynaFlow trains a main agent to write its own multi-agent workflows as code instead of using fixed collaboration structures
DynaFlow integrates free-form executable workflow synthesis into a main agent's action space. The main agent decides when collaboration is needed and generates task-specific programs that coordinate fixed sub-agents through sequential or parallel execution, information exchange, and verification. Only the main agent is trained, via supervised fine-tuning on distilled trajectories followed by refinement on filtered examples. Across six benchmarks covering mathematical reasoning, scientific QA, and multi-hop retrieval, the paper reports 88.08% overall accuracy in Forced mode with a Qwen3.5-27B backbone.
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- Primary sourceHugging Face discovery · source check open
- Discovery linkHugging Face
- Linked in sourceView PDF
- Linked in sourcearXiv:2610.34467v1
- Linked in sourceLearn more about arXivLabs
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DynaFlow integrates free-form executable workflow synthesis into a main agent's action space. The main agent decides when collaboration is needed and generates task-specific programs that coordinate fixed sub-agents through sequential or parallel execution, information exchange, and verification. Only the main agent is trained, via supervised fine-tuning on distilled trajectories followed by refinement on filtered examples. Across six benchmarks covering mathematical reasoning, scientific QA, and multi-hop retrieval, the paper reports 88.08% overall accuracy in Forced mode with a Qwen3.5-27B backbone.
Key findings from the stored brief
- The main agent generates task-specific programs that coordinate fixed sub-agents rather than using predefined collaboration structures.
- Only the main agent is trained; all sub-agents remain fixed to separate orchestration learning from executor capability.
- Reports 88.08% overall accuracy in Forced mode across six benchmarks, 10.90 points above the strongest evaluated baseline.
- Workflow analysis finds more explicit use, verification, and refinement of upstream outputs after training.
Source claims not independently checked; results are reported on the paper's chosen benchmarks and a single backbone model.
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Why this may matter
Learning to write adaptive orchestration programs, instead of relying on fixed agent topologies, could make multi-agent systems more flexible across diverse tasks. Cross-stage information flow is highlighted as a dimension worth studying.
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