Diagnosing and Correcting Emergent Reasoning Pathologies in Chain-of-Thought Models

While Nowak et al. (2024) and Wang et al. (2023) formalize and improve CoT reasoning, there’s still little systematic understanding of where and why reasoning “breaks down” in surprising ways—especially in real-world, open-ended tasks. Building on Chen et al. (2025), who focus on attention-based truthfulness signals, this research would map the space of emergent CoT reasoning errors (e.g., hallucinations, over-justification, cyclical reasoning, or strategic deception as raised by Wang et al. 2025). By combining trace analysis, behavioral testing, and intervention (e.g., trigger targeted prompts or adversarial attacks like Xue et al. 2025), the project would create a taxonomy of failure modes and propose automated repair strategies—potentially using dynamic, stepwise correction or intervention mechanisms. Such work would be transformative for safety, reliability, and model transparency.

References:

1. On the Representational Capacity of Neural Language Models with Chain-of-Thought Reasoning. Franz Nowak, Anej Svete, Alexandra Butoi, Ryan Cotterell (2024). Annual Meeting of the Association for Computational Linguistics.
2. Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models. Lei Wang, Wanyu Xu, Yihuai Lan, Zhiqiang Hu, Yunshi Lan, Roy Ka-Wei Lee, Ee-Peng Lim (2023). Annual Meeting of the Association for Computational Linguistics.
3. Deep Hidden Cognition Facilitates Reliable Chain-of-Thought Reasoning. Zijun Chen, Wenbo Hu, Richang Hong (2025). arXiv.org.
4. Thought Purity: Defense Paradigm For Chain-of-Thought Attack. Zihao Xue, Zhen Bi, Long Ma, Zhen-Hua Hu, Yan Wang, Zhenfang Liu, Qing Sheng, Jie Xiao, Jungang Lou (2025). arXiv.org.
5. When Thinking LLMs Lie: Unveiling the Strategic Deception in Representations of Reasoning Models. Kai Wang, Yihao Zhang, Meng Sun (2025). arXiv.org.