Deep Hidden Cognition Facilitates Reliable Chain-of-Thought Reasoning

• URL: http://arxiv.org/abs/2507.10007v1
• Date: Mon, 14 Jul 2025 07:41:35 GMT
• Title: Deep Hidden Cognition Facilitates Reliable Chain-of-Thought Reasoning
• Authors: Zijun Chen, Wenbo Hu, Richang Hong,
• Abstract summary: Chain of Thought (CoT) reasoning has demonstrated remarkable deep reasoning capabilities.<n>However, its reliability is often undermined by the accumulation of errors in intermediate steps.<n>This paper introduces an approach to calibrate the CoT reasoning accuracy by leveraging the model's intrinsic veracity encoding.
• Score: 33.30315111732609
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Chain of Thought (CoT) reasoning has demonstrated remarkable deep reasoning capabilities in both large language models (LLMs) and multimodal large language models (MLLMs). However, its reliability is often undermined by the accumulation of errors in intermediate steps. This paper introduces an novel approach to calibrate the CoT reasoning accuracy by leveraging the model's intrinsic veracity encoding. We discover that specific attention head activations reliably reflect the truthfulness of reasoning steps in CoT. Based on this insight, we train a confidence predictor to evaluate the correctness of each reasoning step using these truthfulness-sensitive activations, dynamically selecting the most plausible reasoning path via beam search. Experimental results demonstrate that our method significantly outperforms the state-of-the-art baselines (e.g., Few-Shot CoT, Self-Consistency, and Self-Evaluation Guided Beam Search) across the mathematical, symbolic, and commonsense reasoning tasks, exhibiting superior accuracy and reliability in both unimodal and multimodal settings. We further validate the approach on large reasoning models, confirming its applicability to specialized reasoning models. Additionally, we explore the role of the model's self-correction ability in CoT reasoning. This work provides a novel reliability improvement path for CoT reasoning with broad application potential.

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