Trajectory-Adapted Uncertainty Quantification for LLM Agents

A recent preprint on arXiv (2608.11552) explores the application of single-turn uncertainty quantification (UQ) techniques to multi-turn interactions involving large language models (LLMs). The research assesses three UQ methodologies: white-box scorers that analyze action-token probabilities, black-box consistency scorers based on resampled data, and reflexive scorers that depend on self-evaluation by the model. The study examines five LLMs and four datasets from BFCL-v4 and τ²-bench. Results reveal that although these methods can be beneficial, they may not be consistently reliable; consequently, tailored UQ approaches are essential for handling LLM decision-making errors effectively.