arXiv:2605.15588v2 Announce Type: replace-cross Abstract: As large language models (LLMs) are deployed in consequential settings such as medical question answering and legal reasoning, the ability to estimate when their outputs are likely to be correct is essential for safe and reliable use, requiring well-calibrated uncertainty. Standard reinforcement learning with verifiable rewards (RLVR) trains models with a binary correctness reward that is indifferent to confidence, providing no penalty for confident but wrong predictions and thereby degrading calibration. Recent work addresses this by t
As LLMs are increasingly deployed in sensitive, real-world applications, the limitations of current reward systems in ensuring trustworthy and calibrated outputs are becoming critical.
Ensuring LLM outputs are well-calibrated and trustworthy is fundamental for their safe adoption in high-stakes domains like medicine and legal reasoning, directly impacting regulatory acceptance and public trust.
This research introduces a novel approach to LLM calibration that moves beyond binary correctness towards semantic-level reward, offering a path to more reliable and responsible AI systems.
- · AI developers
- · Users of LLM-powered applications
- · Healthcare sector
- · Legal sector
- · Developers neglecting calibration
- · Current RLVR approaches
Improved trust and reliability of large language models in critical applications.
Accelerated adoption of LLMs in regulated industries due to enhanced safety mechanisms.
New regulatory frameworks specifically addressing AI uncertainty quantification and calibration standards.
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