Making LLMs Say What They Think: Measuring and Improving CoT-Interpretability Alignment

arXiv:2609.38972 · cs.CL, cs.AI · Submitted 2026-09-30 · Read on arXiv

cs.CL, cs.AI

Submitted: 2026-09-30

Updated: 2026-09-30

Code: https://github.com/yihuaihong/CIA-minimal-repro

Terminology

• Interpretability — 50× in this paper · explained in 15 episodes
• Accuracy — 32× in this paper · explained in 1 episode
• Linear probe — 31× in this paper · explained in 2 episodes
• GRPO — 23× in this paper · explained in 6 episodes
• Alignment — 20× in this paper · explained in 1 episode
• Trace — 16× in this paper · explained in 2 episodes
• CoT-Interpretability Alignment — 11× in this paper
• Thinking mode — 7× in this paper · explained in 2 episodes
• Generalization — 6× in this paper · explained in 13 episodes
• Transformer — 6× in this paper · explained in 2 episodes
• Consistency — 5× in this paper · explained in 3 episodes
• Rejection Sampling — 5× in this paper
• Chain of Thought — 3× in this paper
• Decode — 3× in this paper · explained in 2 episodes
• LLM judge — 3× in this paper · explained in 2 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Chain-of-Thought (CoT) — 2× in this paper · explained in 4 episodes
• Explainability — 2× in this paper · explained in 3 episodes
• F1 Score — 2× in this paper · explained in 3 episodes
• Latent Reasoning — 2× in this paper · explained in 2 episodes
• Latent Space — 2× in this paper · explained in 7 episodes
• Multi-hop Reasoning — 2× in this paper · explained in 3 episodes
• Self-consistency — 2× in this paper · explained in 2 episodes
• AUC — 1× in this paper · explained in 2 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• AdamW Optimizer — 1× in this paper · explained in 1 episode
• Centers for Disease Control and Prevention — 1× in this paper
• Chain-of-Thought Prompting — 1× in this paper · explained in 2 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Reward Function — 1× in this paper · explained in 2 episodes
• Reward Hacking — 1× in this paper · explained in 8 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Scaling Laws — 1× in this paper · explained in 4 episodes
• Verification — 1× in this paper · explained in 2 episodes

Sources

• Why Can't Transformers Learn Multiplication? Reverse-Engineering Reveals Long-Range Dependency Pitfalls
• Eliciting Latent Predictions from Transformers with the Tuned Lens
• Eight Things to Know about Large Language Models
• Reasoning Models Don't Always Say What They Think
• DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
• Gemma 2: Improving Open Language Models at a Practical Size
• The Llama 3 Herd of Models
• Counterfactual Simulation Training for Chain-of-Thought Faithfulness
• Measuring Faithfulness in Chain-of-Thought Reasoning
• Parcae: Scaling Laws For Stable Looped Language Models
• DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
• Is Chain-of-Thought Really Not Explainability? Chain-of-Thought Can Be Faithful without Hint Verbalization
• Scaling Latent Reasoning via Looped Language Models

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