# Chain-of-Thought Reasoning: Research Digest

> Foundational work showed that step-by-step prompts elicit reasoning in large models. Recent preprints probe what those chains encode, how to verify and distill them, and how to compress them into latent states.

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- **Updated:** 2026-10-16
- **Category:** scholar
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## Key data

- **Field:** chain-of-thought reasoning language models
- **Foundational:** 10
- **Recent (arXiv):** 8
- **Updated:** 2026-10-16

Foundational work showed that step-by-step prompts elicit reasoning in large models. Recent preprints probe what those chains encode, how to verify and distill them, and how to compress them into latent states.

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## Sources

1. [Multimodal chain-of-thought reasoning in language models](https://arxiv.org/abs/2302.00923)
2. [Automatic chain of thought prompting in large language models](https://arxiv.org/abs/2210.03493)
3. [Self-consistency improves chain of thought reasoning in language models](https://arxiv.org/abs/2203.11171)
4. [Plan-and-solve prompting: Improving zero-shot chain-of-thought reasoning by large language models](https://aclanthology.org/2023.acl-long.147/)
5. [Towards reasoning era: A survey of long chain-of-thought for reasoning large language models](https://link.springer.com/article/10.1007/s11432-025-4665-8)
6. [Chain-of-thought prompting elicits reasoning in large language models](https://proceedings.neurips.cc/paper_files/paper/2022/hash/9d5609613524ecf4f15af0f7b31abca4-Abstract.html)
7. [Beyond chain-of-thought, effective graph-of-thought reasoning in language models](https://arxiv.org/abs/2305.16582)
8. [Active prompting with chain-of-thought for large language models](https://aclanthology.org/2024.acl-long.73.pdf)
9. [Interactive reasoning: Visualizing and controlling chain-of-thought reasoning in large language models](https://dl.acm.org/doi/abs/10.1145/3742413.3789091)
10. [Enhancing zero-shot chain-of-thought reasoning in large language models through logic](https://aclanthology.org/2024.lrec-main.543/)
11. [The Density of Thought: Predicting and Steering Reasoning in Large Language Models](https://arxiv.org/abs/2610.21286v1)
12. [Does Latent Chain-of-Thought Actually Reason? A Single, Unifying Taxonomy for Latent Reasoning](https://arxiv.org/abs/2610.21182v1)
13. [CoTranslator: Reverse to Forward Chain-of-Thought Translator for Reasoning Distillation](https://arxiv.org/abs/2610.20828v1)
14. [MolLean: A Process-Verifiable Language for Chemical Reasoning](https://arxiv.org/abs/2610.20459v1)
15. [Argument Graph Uncertainty: Quantifying Uncertainty from the Logical Structure of Reasoning Chains](https://arxiv.org/abs/2610.20328v1)
16. [FACES: Fine-grained Gated Semantic Alignment for Multimodal Emotional Support Conversation](https://arxiv.org/abs/2610.20051v1)
17. [Frustratingly Easy Majority Voting Using Length Conditioning](https://arxiv.org/abs/2610.19890v1)
18. [Rationale Distribution Distillation via Flow Matching for Distributional Continuous Latent Reasoning](https://arxiv.org/abs/2610.19871v1)

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