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A training-free framework that reformulates failure recovery as test-time scaling over causal histories, which decomposes recovery into three coupled decisions: when to revise the causal history, where to recover a reliable history prefix, and which history configuration best supports subsequent execution.
Current VLMs, despite excelling at general reasoning, still fail to accurately identify food and estimate nutrition, even when given multiple views and chain-of-thought prompting.
