AI RESEARCH
Temporal Difference Calibration in Sequential Tasks: Application to Vision-Language-Action Models
arXiv CS.LG
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ArXi:2604.20472v1 Announce Type: cross Recent advances in vision-language-action (VLA) models for robotics have highlighted the importance of reliable uncertainty quantification in sequential tasks. However, assessing and improving calibration in such settings remains mostly unexplored, especially when only partial trajectories are observed. In this work, we formulate sequential calibration for episodic tasks, where task-success confidence is produced along an episode, while success is determined at the end of it. We