From Passive Metric to Active Signal: The Evolving Role of Uncertainty Quantification in Large Language Models
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866917419531894784 |
|---|---|
| author | Zhang, Jiaxin Cui, Wendi Li, Zhuohang Huang, Lifu Malin, Bradley Xiong, Caiming Wu, Chien-Sheng |
| author_facet | Zhang, Jiaxin Cui, Wendi Li, Zhuohang Huang, Lifu Malin, Bradley Xiong, Caiming Wu, Chien-Sheng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_15690 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | From Passive Metric to Active Signal: The Evolving Role of Uncertainty Quantification in Large Language Models Zhang, Jiaxin Cui, Wendi Li, Zhuohang Huang, Lifu Malin, Bradley Xiong, Caiming Wu, Chien-Sheng Artificial Intelligence Applications 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. |
| title | From Passive Metric to Active Signal: The Evolving Role of Uncertainty Quantification in Large Language Models |
| topic | Artificial Intelligence Applications |
| url | https://arxiv.org/abs/2601.15690 |