How Chain-of-Thought Works? Tracing Information Flow from Decoding, Projection, and Activation
cs.AI
Submitted: 2025-07-28
Updated: 2026-08-26
Code: https://github.com/How-Young-X/cot
Terminology
Sources
- Contrastive Chain-of-Thought Prompting
- Training Verifiers to Solve Math Word Problems
- How to think step-by-step: A mechanistic understanding of chain-of-thought reasoning
- How Do Multilingual Language Models Remember Facts?
- The Llama 3 Herd of Models
- ROME: Memorization Insights from Text, Logits and Representation
- LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters!
- Text and Patterns: For Effective Chain of Thought, It Takes Two to Tango
- Concise Thoughts: Impact of Output Length on LLM Reasoning and Cost
- On the Hardness of Faithful Chain-of-Thought Reasoning in Large Language Models
- Gemma 2: Improving Open Language Models at a Practical Size
- Analyzing Chain-of-Thought Prompting in Large Language Models via Gradient-based Feature Attributions
- Towards Faithful Natural Language Explanations: A Study Using Activation Patching in Large Language Models
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