Event-Grounded Sparse Autoencoders for Vision-Language-Action Policies

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 interventions can only be tested via expensive closed-loop rollouts. We propose an event-grounded interpretability pipeline that anchors SAE feature a

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ethics.ai (17 May 2026), “Event-Grounded Sparse Autoencoders for Vision-Language-Action Policies,” evidence record 4207, https://ethics.ai/record/4207 (originally published by arXiv).

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