# Hidden Thoughts Are Not Secret: Reasoning Trace Exposure in LLMs

arXiv: https://arxiv.org/abs/2606.00642 · HTML: https://mlsanity.com/paper/2606.00642/

## Key facts

- Cited by 2 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 14 of its 58 references that are in the map.
- First posted on arXiv 30 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, 14 of 14)

- [Training Verifiers to Solve Math Word Problems](https://mlsanity.com/paper/2110.14168/) (2021)
- [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025)
- [Qwen2.5 Technical Report](https://mlsanity.com/paper/2412.15115/) (2024)
- [Large Language Diffusion Models](https://mlsanity.com/paper/2502.09992/) (2025)
- [Measuring Faithfulness in Chain-of-Thought Reasoning](https://mlsanity.com/paper/2307.13702/) (2023)
- [Dream 7B: Diffusion Large Language Models](https://mlsanity.com/paper/2508.15487/) (2025)
- [Detecting Language Model Attacks with Perplexity](https://mlsanity.com/paper/2308.14132/) (2023)
- [Reasoning Models Don't Always Say What They Think](https://mlsanity.com/paper/2505.05410/) (2025)
- [Monitoring Reasoning Models for Misbehavior and the Risks of Promoting Obfuscation](https://mlsanity.com/paper/2503.11926/) (2025)
- [Orca: Progressive Learning from Complex Explanation Traces of GPT-4](https://mlsanity.com/paper/2306.02707/) (2023)
- [SALT: Steering Activations towards Leakage-free Thinking in Chain of Thought](https://mlsanity.com/paper/2511.07772/) (2025)
- [How to Steal Reasoning Without Reasoning Traces](https://mlsanity.com/paper/2603.07267/) (2026)
- [Chain-of-Sanitized-Thoughts: Plugging PII Leakage in CoT of Large Reasoning Models](https://mlsanity.com/paper/2601.05076/) (2026)
- [Safer Reasoning Traces: Measuring and Mitigating Chain-of-Thought Leakage in LLMs](https://mlsanity.com/paper/2603.05618/) (2026)

## Built on by (most cited first, 2 of 2)

- [Capable yet Parsimonious: Extracting and Characterizing Hidden Chain-of-Thought in Frontier Models](https://mlsanity.com/paper/2609.26637/) (2026)
- ["Nothing to See Here'': Unintended Disclosure through Revision Traces of LLM Deliverables](https://mlsanity.com/paper/2609.35408/) (2026)

## Abstract (arXiv)

Reasoning traces have become a valuable form of learning signals for improving and transferring the capabilities of large language models. In particular, detailed traces can help distill reasoning behavior from stronger teacher models into weaker student models. The value of capability transfer has motivated many deployed systems with reasoning models to hide raw internal traces and expose at most summaries and answers to users. As a result, we ask whether such interface-level trace hiding prevents users from obtaining useful reasoning supervision through prompting. We study this question with Reasoning Exposure Prompting (REP), a lightweight in-context elicitation method that uses shadow-model-generated demonstrations wrapped in auxiliary code-like formats to raise user-visible reasoning traces from a victim model. Across the common reasoning dataset, different victim models, and different student model distillation, REP substantially increases similarity between exposed and REP-conditioned internal traces while preserving useful reasoning signals.