PrefixGuard: Online Failure Warning and Trace-Grounded Diagnosis for LLM Agents
Large language model (LLM) agents execute multi-step tasks, but terminal verdicts arrive too late for intervention. Agent traces mix messages, tool calls, and feedback, making it difficult to learn failure signals from terminal outcomes. Even if a model learns to predict failure from execution prefixes, its predictions alone cannot show which actions, feedback, or unmet conditions need inspection. Existing work studies online failure prediction and diagnosis from completed trajectories, yet few methods connect timely warning to a compact symbolic model of execution behavior. We introduce \textbf{PrefixGuard}, a neuro-symbolic method that uses learned events to connect online failure warni...
| Authors | Xinmiao Huang, Jinwei Hu, Qisong He et al. |
|---|---|
| Primary category | cs.AI |
| Published | 7 May 2026 |
| Last updated at source | 27 September 2026 |
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