Article

Failure Attribution for VLA Models: Estimating the Benefit of Removing Visual Corruptions

The paper defines the problem of failure attribution for vision-language-action (VLA) models: when multiple visual corruptions coexist, determining which corruptions contribute to failure risk and how much benefit their removal would bring. The authors propose a factorized risk model that learns separate representations of the scene and of corruptions, and estimates removal benefits through latent intervention using only the current image and task instruction, without paired clean images or additional counterfactual rollouts.

Part of the event: Proposes a visual failure attribution method for VLA models

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