arXiv:2605.21127v1 Announce Type: new Abstract: Explicit reasoning models are trained to produce intermediate reasoning traces before final answers, but downstream fine-tuning is often performed on ordinary instruction-response data that contains no such traces. We show that this mismatch can induce reasoning-trace collapse: a fine-tuned model continues to produce plausible final answers while losing the structurally valid explicit reasoning traces that made it a reasoning model in the first place. We introduce a structural evaluation framework that separates answer correctness from reasoning-
The proliferation of complex AI models and fine-tuning practices makes understanding the integrity of explicit reasoning increasingly critical to avoid 'black box' issues.
This research highlights a crucial fragility in advanced AI models, where fine-tuning can inadvertently degrade their ability to show transparent reasoning, impacting trust and reliability.
The understanding of AI model fidelity during fine-tuning changes, emphasizing the need for new evaluation frameworks to preserve explicit reasoning capabilities, not just final answer correctness.
- · AI evaluation companies
- · Developers of robust fine-tuning methods
- · AI transparency advocates
- · Sectors requiring high AI explainability (e.g., finance, healthcare)
- · AI developers using naive fine-tuning
- · Companies relying solely on answer correctness for model deployment
- · Models without explicit reasoning capabilities
AI models fine-tuned without care for reasoning traces will become less explainable and trustworthy.
New industry standards and tools will emerge for evaluating and preserving explicit reasoning during AI model adaptation.
Regulatory bodies may mandate explicit reasoning traceability for AI systems deployed in critical applications, driving innovation in interpretability.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG