Hidden Thoughts Are Not Secret: Reasoning Trace Exposure in LLMs
Quick summary
arXiv:2606.00642v2 Announce Type: replace Abstract: 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 superv
Key takeaways
- arXiv:2606.00642v2 Announce Type: replace Abstract: 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.
Why it matters
“Hidden Thoughts Are Not Secret: Reasoning Trace Exposure in LLMs” should be evaluated beyond branding and benchmark scores. Its practical importance will emerge in task accuracy, latency, unit cost, safety and integration with real workflows.
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