Towards Reducible Uncertainty Modeling for Reliable Large Language Model Agents

2026-02-04

AI Summary

This work addresses the limitations of existing uncertainty quantification (UQ) methods for large language models (LLMs), which predominantly focus on single-turn question answering and are ill-suited for complex tasks in open-world interactive agents. The paper introduces the first general-purpose UQ framework tailored for LLM-based agents, conceptualizing uncertainty as a dynamic process that can be progressively reduced through interactive actions. It proposes a novel paradigm—conditional uncertainty reduction—that overcomes the constraints of traditional cumulative modeling approaches. By integrating probabilistic modeling, trajectory analysis, and conditional reasoning, the framework highlights the critical role of agent actions in mitigating reducible uncertainty. This approach provides a theoretical foundation for designing reliable interactive agents and demonstrates promising deployment potential across state-of-the-art models and real-world applications.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Uncertainty Representations
• Multiagent Systems: Multiagent Systems under Uncertainty

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Large language models for search

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 the first general formulation of agent UQ that subsumes broad classes of existing UQ setups. Under this formulation, we show that prior works implicitly treat LLM UQ as an uncertainty accumulation process, a viewpoint that breaks down for interactive agents in an open world. In contrast, we propose a novel perspective, a conditional uncertainty reduction process, that explicitly models reducible uncertainty over an agent's trajectory by highlighting"interactivity"of actions. From this perspective, we outline a conceptual framework to provide actionable guidance for designing UQ in LLM agent setups. Finally, we conclude with practical implications of the agent UQ in frontier LLM development and domain-specific applications, as well as open remaining problems.

Problem

• Uncertainty Quantification
• Large Language Models
• Interactive Agents
• Reducible Uncertainty
• Agent Safety

Innovation

• uncertainty quantification
• large language model agents
• reducible uncertainty
• interactive agents
• conditional uncertainty reduction