What Happens Inside VLAs for Compositional Generalization? A Mechanistic Analysis

Vision-language-action (VLA) policies show strong performance on many manipulation benchmarks, but they fail to generalize compositionally: even when objects and skills are familiar, visual shortcuts or isolated object words can dominate the full instruction. Prior work develops mitigation methods based on hypothesized failure mechanisms. We instead analyze internal representations to understand when and where task-relevant information supports action generation. We study how task information flows from text prompts to final actions and what happens internally when compositional generalization fails. We introduce VLA-PatchLens, an analysis framework based on activation patching and path patching for localizing task-critical information. The framework compares matched clean and corrupted runs that differ in task-critical instructions or task behavior. For each matched pair, we replace the corrupted run’s activation at a layer-token group with the clean-run activation and measure whether the patch recovers action loss and rollout success. We construct three compositional generalization task suites, and for π₀.₅, the analysis shows that task-critical information in text tokens is evenly distributed across layers, with early and late layers carrying complementary parts of the task; in later layers, early text-token information becomes concentrated in image tokens and reaches action tokens through cross-attention. We also find that action-loss recovery and success-rate recovery are highly consistent, making action-loss recovery a lightweight proxy for selecting intervention targets before closed-loop evaluation. In compositional generalization failures, VLA-PatchLens reveals clear breaks in this task-critical information flow, and finds simple single-layer activation patching improves success rate from 0% to 3.9–23.5% on average and up to 66.7% at the best layer across three task suites, while activation-similarity scores remain uninformative.