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arXiv · 2601.15690

From Passive Metric to Active Signal: The Evolving Role of Uncertainty Quantification in Large Language Models

Jiaxin Zhang·Wendi Cui·Zhuohang Li·Lifu Huang·Bradley Malin·Caiming Xiong·Chien-Sheng Wu
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Abstract

While Large Language Models (LLMs) show remarkable capabilities, their unreliability remains a critical barrier to deployment in high-stakes domains. This survey charts a functional evolution in addressing this challenge: the evolution of uncertainty from a passive diagnostic metric to an active control signal guiding real-time model behavior. We demonstrate how uncertainty is leveraged as an active control signal across three frontiers: in \textbf{advanced reasoning} to optimize computation and trigger self-correction; in \textbf{autonomous agents} to govern metacognitive decisions about tool use and information seeking; and in \textbf{reinforcement learning} to mitigate reward hacking and enable self-improvement via intrinsic rewards. By grounding these advancements in emerging theoretical frameworks like Bayesian methods and Conformal Prediction, we provide a unified perspective on this transformative trend. This survey provides a comprehensive overview, critical analysis, and practical design patterns, arguing that mastering the new trend of uncertainty is essential for building the next generation of scalable, reliable, and trustworthy AI.

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Jiaxin Zhang, Wendi Cui, Zhuohang Li, Lifu Huang, Bradley Malin, Caiming Xiong, Chien-Sheng Wu. 2026-01-22. From Passive Metric to Active Signal: The Evolving Role of Uncertainty Quantification in Large Language Models. https://arxiv.org/abs/2601.15690

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