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Do VLAs Know They Will Fail? Interpretable Success and Failure Patterns in Physical AI Systems

Research shows that sparse autoencoder (SAE) representations of OpenVLA activations contain outcome-predictive information and can predict task failure before the robot reaches an unrecoverable state. The authors also identify individual SAE features associated with specific stages of success or failure, and uncover recurring behavioral patterns by localizing temporal intervals where these features show significant activation changes. The team further builds a VLM-based interpretation pipeline to generate behavioral descriptions of these patterns and validates their correspondence to feature activations on held-out intervals.

Part of the event: SAE Representations Can Predict VLA Task Failure in Advance

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