Tri-Info: Generalizable, Interpretable Failure Prediction for VLA Models via Information Theory

Name: Tri-Info: Generalizable, Interpretable Failure Prediction for VLA Models via Information Theory
Uploaded: 2026-10-02T04:22:52.748583+00:00
Duration: 424 s
Description: TriInfo is a new information-theoretic framework that analyzes VLA control as an information pipeline to detect failures in robotics models. It derives three key signals—action diversity, temporal consistency, and action–state coupling—which capture systematic differences between successes and failures. This method is highly generalizable across different systems without retraining.

TriInfo is a new information-theoretic framework that analyzes VLA control as an information pipeline to detect failures in robotics models. It derives three key signals—action diversity, temporal consistency, and action–state coupling—which capture systematic differences between successes and failures. This method is highly generalizable across different systems without retraining.

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