Tools as Continuous Flow for Evolving Agentic Reasoning
Tairan Huang, Siyu Shang, Qiang Chen, Xiu Su, Yi Chen
Why It Matters
What makes this one worth your time
This research addresses critical limitations in existing reasoning frameworks, potentially leading to more effective applications of LLMs in complex, real-world scenarios.
FlowAgent redefines tool chaining for enhanced reasoning in dynamic environments.
Summary
The paper introduces FlowAgent, a new paradigm for tool chaining in reasoning tasks that emphasizes continuous trajectory generation in a semantic space, aiming to improve robustness and adaptability in long-horizon reasoning.
Key contributions
- Introduction of the FlowAgent framework for continuous tool chaining.
- Development of a plan-level closed-loop benchmark for evaluating agentic reasoning.
- Establishment of formal bounds on utility convergence for the proposed method.
Notable insights
- The concept of continuous trajectory generation offers a novel approach to mitigating error accumulation in reasoning tasks.
- Conditional flow matching as a mechanism for generating latent trajectories is a unique methodological contribution.
Possible limitations
- Not stated in the abstract.
Abstract
arXiv:2605.07339v2 Announce Type: replace Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities in orchestrating tools for reasoning tasks. However, existing methods rely on a step-wise paradigm that lacks a global perspective, which causes error accumulation over long horizons and restricts generalization to unseen tools. To overcome these limitations, we propose Tools as Continuous Flow for Evolving Agentic Reasoning (FlowAgent), which reconceptualizes tool chaining as continuous trajectory generation within a semantic space. To systematically evaluate this paradigm, we introduce the first plan-level closed-loop benchmark dedicated to plan-level agentic reasoning in dynamic real-world environments. Specifically, the proposed FlowAgent leverages conditional flow matching to generate continuous latent trajectories, providing a global planning perspective to ensure coherent and robust tool execution. Theoretically, we establish formal bounds on utility convergence and prove that our continuous formulation fundamentally guarantees robust generalization and error attenuation. Empirical evaluations show that FlowAgent achieves superior robustness and adaptability in long-horizon reasoning tasks.