arXiv: cs.AI preprintsInternational6 November 2026
Event-Grounded Sparse Autoencoders for Vision-Language-Action Policies
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arXiv:2605.17204v2 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) policies translate language and visual inputs into robot actions, where their hidden representations directly shape closed-loop behavior. However, mechanistic interpretability tools from language and vision-language models do not transfer cleanly to VLAs: outputs are robot actions rather than human-readable tokens, and validating internal changes requires expensive closed-loop rollouts. We propose an event-grounded interpretability pipeline that anchors SAE feature analysis to behavioral events rather than t
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