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This work introduces PrefixGuard, a neuro-symbolic method that uses learned events to connect online failure warning with finite-state execution diagnosis with finite-state execution diagnosis in large language model (LLM) agents.
The findings reveal that pretrained object detectors already encode substantially richer latent knowledge than is typically exploited for OoD detection, and this knowledge can be explicitly elicited and organized into a compact, structured, and interpretable knowledge space for prediction reliability analysis.
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