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#artificial intelligence Preprint Sep 2026

Counterfactual Tests for Measuring Chain-of-Thought Faithfulness in Visual Language Models

The analysis shows that CoTs do not reliably track visual evidence that influences model predictions, and it is found that Predict-then-Explain explanations align more strongly with perturbation-induced probability shifts than pre-answer CoTs, while binary vCT scores are often nearly saturated.

Bayar Menzat, Max Süss, Rui-Zhi Wang et al. · 0 citations

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