AINeutralarXiv – CS AI · 9h ago6/10
🧠
When Evidence is Sparse: Weakly Supervised Early Failure Alerting in Dialogs and LLM-Agent Trajectories
Researchers present a weakly supervised approach for detecting dialog and agent failures early in their execution, introducing an attention-based predictor that identifies sparse failure evidence and pairs it with a preference-conditioned stopping policy. The method achieves 3-42% improvement over existing approaches while reducing training costs by 1-3 orders of magnitude across five benchmarks.