Locating and Repairing Domain Shift in VLM Trajectory Planning

Zhihong Cui, Hengyu Liu, Michael Alexander Riegler, Guandong Xu, Amir Taherkordi +1

Vision-Language Models (VLMs) are increasingly adopted as end-to-end driving planners, yet they suffer from cross-city domain shift. Existing methods treat this shift as a single quantity addressed by uniform alignment or single-site editing. We challenge this view on two counts: (i) domain shift in VLM trajectory planners is inherently structured across modality and layer, and (ii) a locate-then-repair recipe matched to this structure outperforms every uniform-objective baseline. We analyse domain shift via activation patching and uncover two regularities. (F1) Modality axis. Image and text tokens exhibit distinct shift patterns in each VLM. (F2) Layer axis. The layer-wise shift partition is architecture-dependent and dataset-stable. These findings recast domain shift as a two-dimensional tensor indexed by modality (image vs. text tokens) and layer, motivating a simple principle: locate where shift occurs, and repair only there. We instantiate this principle in MoLaRx (Modality-Layer Repair), a locate-then-repair framework. The Causal Importance Score (CIS) estimates the domain shift as a modality-layer tensor and partitions the layers into functional zones. CIS-Guided Selective…