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

TL;DR

This paper emphasizes the importance of uncertainty quantification for large language model agents, proposing a new framework, identifying key challenges, and discussing future research directions.

Contribution

It introduces the first general formulation of agent UQ, highlights four specific challenges, and provides analysis on a real-world benchmark.

Findings

Presented the first general formulation of agent UQ

Identified four key technical challenges in agent UQ

Provided numerical analysis on the $ au^2$-bench benchmark

Abstract

Uncertainty quantification (UQ) for large language models (LLMs) is a key building block for safety guardrails of daily LLM applications. Yet, even as LLM agents are increasingly deployed in highly complex tasks, most UQ research still centers on single-turn question-answering. We argue that UQ research must shift to realistic settings with interactive agents, and that a new principled framework for agent UQ is needed. This paper presents three pillars to build a solid ground for future agent UQ research: (1. Foundations) We present the first general formulation of agent UQ that subsumes broad classes of existing UQ setups; (2. Challenges) We identify four technical challenges specifically tied to agentic setups -- selection of uncertainty estimator, uncertainty of heterogeneous entities, modeling uncertainty dynamics in interactive systems, and lack of fine-grained benchmarks -- with numerical analysis on a real-world agent benchmark, $\tau^2$-bench; (3. Future Directions) We conclude with noting on the practical implications of agent UQ and remaining open problems as forward-looking discussion for future explorations.