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October 2, 2025Open Access

Deep Hidden Cognition Facilitates Reliable Chain-of-Thought Reasoning

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Authors

ZCZijun ChenWHWenbo HuRHRichang Hong

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Overview

This approach enhances deep reasoning outcomes in large models, demonstrating improved accuracy and reliability.

Key Points

  • The proposed method significantly improves the reliability of chain of thought reasoning, and enhances accuracy.
  • Experimental results show that this method outperforms current state-of-the-art approaches in various reasoning tasks.
  • A confidence predictor evaluates reasoning step correctness using truthfulness-sensitive activations for improved outcomes.
  • The approach validates its effectiveness across both unimodal and multimodal large language models.

Cite This Study

Chen et al. (2025) studied this question.

synapsesocial.com/papers/68de6f3a83cbc991d0a2291bhttps://doi.org/10.48550/arxiv.2507.10007
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1A Hopfieldian View-based Interpretation for Chain-of-Thought Reasoning2024
  2. 2From Reasoning to Super-Intelligence: A Search-Theoretic Perspective2025
  3. 3Calibrating Reasoning in Language Models with Internal Consistency2024 · 3 citations
  4. 4Lexical hints of accuracy in LLM reasoning chains2026
  5. 5Multimodal Chain-of-Thought Reasoning in Language Models2023 · 100 citations