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

Paper · 2026 · Changdae Oh, Seongheon Park, To Eun Kim et al.

Summary

Most uncertainty quantification (UQ) work for LLMs studies a model that answers once, or follows one chain of reasoning, and gets no new information along the way. LLM agents work differently. They interact over many turns with users, tools and databases, and their actions can be costly or impossible to undo. This position paper argues that agent UQ is a problem of its own and organizes it around three pillars. First, it gives a formal model: the agent's trajectory is a stochastic process of actions, observations and environment states, drawn as a dynamic Bayesian network. Uncertainty is then defined both per turn and over the whole trajectory, and many earlier UQ setups turn out to be special cases. Second, it names four open technical challenges and backs them with numerical analyses on τ²-bench using GPT-4.1 and Kimi-K2.5. Third, it discusses where agent UQ could be applied and what problems remain unsolved. The goal is a common foundation for building agents that know when to ask for more information and when they can act safely.

Key points

  • Formal model: each turn the policy π, using tool set T, chooses an action from the previous state and observation. The environment then returns an observation, and a deterministic function h updates the state, which holds the dialogue memory plus a database the agent can only partly see. The trajectory probability splits into per-turn action and observation terms.
  • Four challenges:
    • choosing an uncertainty estimator under agentic constraints;
    • estimating uncertainty for content produced by other entities with different distributions;
    • modeling uncertainty dynamics, where uncertainty can shrink and simple weighted averaging falls short;
    • the lack of fine-grained, turn-level benchmarks.
  • Experiments: run on τ²-bench (Barres et al., 2025) with GPT-4.1 and Kimi-K2.5.
  • Application areas: healthcare, software engineering, robotics, adaptive reasoning and post-training.
  • Open problems and limits: UQ for multi-agent systems, self-improving agents that span many episodes, and environments with unreliable or adversarial observations, which the current formulation does not cover.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Carnegie Mellon UniversityPittsburgh · United States539 works · 179,533 citations🎓University of California, BerkeleyBerkeley · United States175 works · 227,440 citations🎓University of PennsylvaniaPhiladelphia · United States221 works · 63,234 citations🎓University of Southern CaliforniaLos Angeles · United States162 works · 35,836 citations🎓University of Wisconsin–MadisonMadison · United States42 works · 48,486 citations

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 works

Cite

Cite as Changdae Oh, Seongheon Park, To Eun Kim, Jiatong Li, Wendi Li, Samuel Yeh, Sean Du, Hamed Hassani, Paul Bogdan, Dawn Song and Sharon Li. 2026. Uncertainty Quantification in LLM Agents: Foundations, Emerging Challenges, and Opportunities. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.acl-long.738

@inproceedings{oh2026uncertainty,
  title = {{Uncertainty Quantification in LLM Agents: Foundations, Emerging Challenges, and Opportunities}},
  author = {Changdae Oh, Seongheon Park, To Eun Kim, Jiatong Li, Wendi Li, Samuel Yeh, Sean Du, Hamed Hassani, Paul Bogdan, Dawn Song and Sharon Li},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.738},
  url = {https://doi.org/10.18653/v1/2026.acl-long.738}
}

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