Leveraging Trajectory Graphs for Pre-Execution Error Diagnosis in Agentic LLM Systems
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, such as 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 \textit{Trajectory Graph Copilot}, a novel framework ...
| Authors | Xu Zheng, Zhuomin Chen, Chaohao Lin et al. |
|---|---|
| Primary category | cs.AI |
| Published | 29 July 2026 |
| Last updated at source | 29 July 2026 |
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