arXiv:2606.24281v1 Announce Type: cross Abstract: Reasoning language models are increasingly asked not only to answer difficult questions, but also to estimate their likelihood of success. Existing methods typically elicit confidence only once: either before thinking or after answering. We argue that confidence in reasoning models is state-dependent: before thinking, confidence should estimate the chance of the model correctly solving the prompt, while after thinking it should predict whether the realized answer is likely to be correct. This distinction determines the appropriate supervision t
The increasing complexity of large language models and their deployment in critical applications necessitates better understanding and calibration of their internal confidence mechanisms, especially as AI agents become more prevalent.
Improving how AI models assess and communicate their confidence is crucial for building trust, enabling more reliable autonomous systems, and defining clear boundaries for their application.
The development of state-dependent confidence calibration methods will lead to more nuanced and trustworthy AI outputs, shifting from a single confidence score to a more contextualized assessment.
- · AI developers
- · AI safety researchers
- · Enterprises adopting AI
- · Autonomous system operators
- · Developers of simplistic confidence metrics
- · Applications requiring absolute certainty from AI
Language models will provide more informative confidence scores, distinguishing between confidence in problem-solving ability and confidence in a specific answer's correctness.
This improved transparency will accelerate the adoption of AI in high-stakes environments where understanding model uncertainty is paramount.
The ability of AI to accurately self-assess and communicate uncertainty could pave the way for more robust human-AI collaboration and self-correcting AI systems.
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Read at arXiv cs.AI