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Vision-language models (VLMs) have achieved strong performance in multimodal reasoning, yet they remain prone to generating plausible but incorrect answers. Self-verification offers a practical way to improve answer reliability without relying on external judges, but existing methods typically depend on a single verifi...
Newton-Schulz Attention (NS-Attn), a parameter-free transformation applied to the output of each attention head, introduces Newton-Schulz Attention (NS-Attn.), a parameter-free transformation applied to the output of each attention head.
This work proposes a Localize-Then-Decide framework, which restores the monotonic relationship between confidence and disagreement risk and enables high-probability agreement guarantees in large language models.
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