When Evidence is Sparse: Weakly Supervised Early Failure Alerting in Dialogs and LLM-Agent Trajectories
arXiv:2606.05414v1 Announce Type: new Abstract: Early failure alerting requires deciding, while a dialog or agent trajectory is still unfolding, whether to flag it as likely to fail. This is challenging because supervision is typically available only as a trajectory-level success/failure label while alerts must be raised from partial interactions. Prior early-classification methods often bridge this gap by assigning the terminal label to every prefix, treating every turn as failure evidence. We hypothesize that this prefix-label assumption is poorly matched to multi-turn language interactions,
The proliferation of LLM-driven agents and conversational AI systems necessitates robust methods for identifying failures early to ensure reliable deployment and user experience.
Improving the accuracy and timeliness of failure detection in AI agents will significantly enhance their practical utility and reduce the costs associated with post-hoc error correction.
Current approaches to identifying AI agent failures at the turn level are being refined, moving beyond simplistic prefix-labeling assumptions to more sophisticated weak supervision techniques.
- · AI developers
- · Companies deploying AI agents
- · Users of conversational AI
- · Companies with unreliable AI systems
- · Traditional error detection methodologies
More resilient and trustworthy AI agents become available for various applications.
Accelerated adoption of AI agents in critical industries due to enhanced reliability.
Increased competition among AI providers to offer agents with superior failure detection capabilities.
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Read at arXiv cs.CL