Title: Trajectory Graph Copilot: Pre-Action Error Diagnosis in LLM Agents
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: LLM Agent, Graph Neural Network, Error Diagnosis

Abstract: Large language model(LLM)-based agents have demonstrated exceptional performance across a wide range of complex interactive tasks. 
However, they often struggle with long-horizon interactive tasks common in domains like embodied AI. The complexity and vast action spaces in these settings lead to compounding errors, where a single suboptimal action can derail an entire trajectory, causing the agent to exhaust its limited step budget on inefficient or unrecoverable paths.
To overcome this without costly fine-tuning, we draw inspiration from software debugging, where execution logs are analyzed to preemptively catch errors. We propose Trajectory Graph Copilot , a novel framework that acts as a ``copilot'' for LLM agents by diagnosing potential action errors before they are executed. At its core, Gebugger models historical trajectories as a probabilistic graph and uses a Graph Neural Network to identify sequential action patterns that frequently lead to failure. Functioning as a proactive diagnostic sandbox, our method provides early warnings on potentially flawed actions, prompting the agent to self-correct. This pre-action error diagnosis prevents costly mistakes, significantly enhancing the agent's ability to complete long-horizon tasks successfully.
The extensive experiments on four benchmarks with three LLM agents demonstrate a $14.69\%$ pass ratio improvement on average.