

















Abstract:Chain-of-Thought (CoT) prompting significantly enhances model reasoning, yet its internal mechanisms remain poorly understood. We analyze CoT's operational principles by reversely tracing information flow across decoding, projection, and activation phases. Our quantitative analysis suggests that CoT may serve as a decoding space pruner, leveraging answer templates to guide output generation, with higher template adherence strongly correlating with improved performance. Furthermore, we surprisingly find that CoT modulates neuron engagement in a task-dependent manner: reducing neuron activation in open-domain tasks, yet increasing it in closed-domain scenarios. These findings offer a novel mechanistic interpretability framework and critical insights for enabling targeted CoT interventions to design more efficient and robust prompts. We released our code and data at this https URL.
| Comments: | Accept by ACL 2026 |
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2507.20758 [cs.AI] |
| (or arXiv:2507.20758v2 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2507.20758 arXiv-issued DOI via DataCite |
From: Qinghua Zhao [view email]
[v1]
Mon, 28 Jul 2025 12:11:16 UTC (1,150 KB)
[v2]
Tue, 26 May 2026 06:42:14 UTC (959 KB)
此内容由惯性聚合(RSS阅读器)自动聚合整理,仅供阅读参考。 原文来自 — 版权归原作者所有。