Work

Uncertainty Quantification in LLM Agents: Foundations, Emerging Challenges, and Opportunities

Year: 2026

Type: conference-paper

Abstract: Changdae Oh, Seongheon Park, To Eun Kim, Jiatong Li, Wendi Li, Samuel Yeh, Sean Du, Hamed Hassani, Paul Bogdan, Dawn Song, Sharon Li. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Source: Proceedings of the conference - Association for Computational Linguistics. Meeting

DOI: 10.18653/v1/2026.acl-long.738

Authors: Changdae Oh, Seongheon Park, To Eun Kim, Jiatong Li, Wendi Li, Samuel Yeh, Sean Du, Hamed Hassani, Paul Bogdan, Dawn Song, Sharon Li

Institutions: University of Wisconsin–Madison, Carnegie Mellon University, University of Pennsylvania, University of Southern California, University of California, Berkeley

Language: English

Topic: Adversarial Robustness in Machine Learning

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funders: National Science Foundation, Open Philanthropy Project, National Institutes of Health, Defense Advanced Research Projects Agency, Advanced Research Projects Agency, National Institute on Aging, Office of Naval Research, Air Force Office of Scientific Research, Army Research Office

Awards: G1304932662, G1523888516, CENTER FOR COMPLEX PARTICLE SYSTEMS (COMPASS), SLES: Foundations of Safety-Aware Learning in the Wild, G6098521345, G6108913550, G6602531639, G7452299184, CAREER: Foundations of Human-Centered Machine Learning in the Wild, G8876996369, MCA:Theoretical Foundations of NeuroAI: A Modeling Framework Motivated by Living Neuronal Network Dynamics, Interpretable machine learning to synergize brain age estimation and neuroimaging genetics

OpenAlex ID: W7166872768

FWCI: 17.22

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Cited by: 2

Related to: 10