# Reasoning that Travels: Dissecting How Chain-of-Thought Transfers Across Models

arXiv: https://arxiv.org/abs/2605.28913 · HTML: https://mlsanity.com/paper/2605.28913/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Prompting and chain-of-thought reasoning, one of 17 directions in LLM reasoning.
- Builds on 16 of its 33 references that are in the map.
- First posted on arXiv 27 May 2026.

## Placed in

- LLM reasoning / [Prompting and chain-of-thought reasoning](https://mlsanity.com/lr/prompting-and-chain-of-thought-reasoning/)

## Builds on (most cited first, 16 of 16)

- [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025)
- [gpt-oss-120b & gpt-oss-20b Model Card](https://mlsanity.com/paper/2508.10925/) (2025)
- [OpenAI o1 System Card](https://mlsanity.com/paper/2412.16720/) (2024)
- [Measuring Faithfulness in Chain-of-Thought Reasoning](https://mlsanity.com/paper/2307.13702/) (2023)
- [Reasoning Models Don't Always Say What They Think](https://mlsanity.com/paper/2505.05410/) (2025)
- [Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models](https://mlsanity.com/paper/2503.09567/) (2025)
- [Chain of Thought Monitorability: A New and Fragile Opportunity for AI Safety](https://mlsanity.com/paper/2507.11473/) (2025)
- [Thought Anchors: Which LLM Reasoning Steps Matter?](https://mlsanity.com/paper/2506.19143/) (2025)
- [Olmo 3](https://mlsanity.com/paper/2512.13961/) (2025)
- [Decoupling the Effect of Chain-of-Thought Reasoning: A Human Label Variation Perspective](https://mlsanity.com/paper/2601.03154/) (2026)
- [Do explanations generalize across large reasoning models?](https://mlsanity.com/paper/2601.11517/) (2026)
- [Measuring Weak-to-Strong Legibility of Reasoning Models](https://mlsanity.com/paper/2603.20508/) (2026)
- [Evaluating Chain-of-Thought Reasoning through Reusability and Verifiability](https://mlsanity.com/paper/2602.17544/) (2026)
- [ConfSpec: Efficient Step-Level Speculative Reasoning via Confidence-Gated Verification](https://mlsanity.com/paper/2602.18447/) (2026)
- [CoT-X: An Adaptive Framework for Cross-Model Chain-of-Thought Transfer and Optimization](https://mlsanity.com/paper/2511.05747/) (2025)
- [TrigReason: Trigger-Based Collaboration between Small and Large Reasoning Models](https://mlsanity.com/paper/2604.14847/) (2026)

## Built on by (most cited first, 1 of 1)

- [PreciseCoder: Faithful and Generalizable Debugging Beyond Unit-Test Rewards](https://mlsanity.com/paper/2610.18755/) (2026)

## Abstract (arXiv)

Large reasoning models (LRMs) often generate extensive chain-of-thought (CoT) traces before producing a final answer. As explicit textual artifacts, these traces can be passed to other models to solve the same task, enabling cross-model reasoning transfer. Yet successful transfer alone does not reveal how the provided CoT contributes to another model's answer. We study this question with a controlled provider--receiver framework, where a provider generates a reasoning trace and a receiver solves the same problem from increasingly longer trace prefixes. We compare force-answer, where the receiver answers directly from the prefix, with free-generation, where it may continue reasoning before answering. Across models and benchmarks, full traces often transfer successfully, but prefix trajectories reveal distinct mechanisms. In force-answer mode, AIME transfer is largely driven by explicit answer availability. MMLU-Pro instead reflects a larger role for receiver competence, while ZebraLogic depends on partial structured-answer information rather than complete-answer leakage alone. In free-generation mode, partial CoTs improve performance across benchmarks, indicating that prefixes can guide continued reasoning. Finally, answer agreement among receivers provides a gold-free signal for stopping provider reasoning early. Overall, cross-model CoT transfer is not a single phenomenon: it can reflect answer extraction, reasoning scaffolding, or receiver-dependent competence.