FlowAgent: Continuous Flow for LLM Tool Orchestration
A new AI framework, FlowAgent, redefines how large language models chain tools by treating the process as continuous trajectory generation in semantic space, overcoming step-wise error accumulation. The method uses conditional flow matching for global planning, with formal bounds on utility convergence. A plan-level closed-loop benchmark for dynamic environments is introduced.
Key facts
- FlowAgent reconceptualizes tool chaining as continuous trajectory generation
- Uses conditional flow matching for global planning perspective
- Introduces first plan-level closed-loop benchmark for agentic reasoning
- Establishes formal bounds on utility convergence
- Addresses error accumulation over long horizons and generalization to unseen tools
- Published as arXiv:2605.07339v1
- Proposed by authors of the paper
- Aims to improve LLM reasoning in dynamic real-world environments
Entities
Institutions
- arXiv