ChunkTrust: Adapting Execution Horizons for Robot Policies with Action-Expert Evidence

Robot foundation policies predict action chunks, but how many actions to execute before replanning depends on the current task phase. We introduce ChunkTrust, which treats the execution horizon as a latent variable inferred from action-expert evidence rather than a fixed hyperparameter. Its training-free Action-aware Horizon Selector (AHS) combines intra-chunk spectral stability of generation traces with inter-chunk continuity between executed history and predicted actions. An online Beta posterior with kernel forgetting tracks horizon preferences across replans. A lightweight Query-based Horizon Adapter (QHA) optionally distills AHS preferences into a dense learned prior, which is fused with fresh online evidence while the base policy remains frozen. Across RoboTwin2.0 and RoboCasa GR1 Tabletop, AHS improves aggregate success for every evaluated base-policy setting, including gains of $+6.80$ percentage points on ${\pi }_{0.5}$ over all 50 RoboTwin2.0 tasks and $+9.67$ percentage points on Qwen3GR00T in RoboCasa. AHS+QHA raises the gain over Base to $+9.44$ percentage points on the eight-task ${\pi }_{0.5}$ evaluation. On four real-world household tasks, AHS improves the equal-task mean normalized process score from 50.4% to 57.5%. Ablations examine the contributions of both evidence terms, temporal memory, and the learned prior.