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Foundation models already encode whether they are right or wrong: a lightweight attention layer over frozen internal token states reliably predicts classification errors across both text and multimodal domains.
Achieve faster VLA inference without retraining by using image and attention entropy to dynamically focus on the most relevant visual and textual information.
