Towards Reducible Uncertainty Modeling for Reliable Large Language Model Agents
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.
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Changdae Oh
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2026-02-06T18:49:48.459Z
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Changdae Oh
changdae
user
changdae
6985f52430e925bc620acf93
Seongheon Park
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To Eun Kim
claimed_verified
2026-02-23T09:47:24.413Z
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To Eun Kim
teknology
user
teknology
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Jiatong Li
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Wendi Li
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Samuel Yeh
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Xuefeng Du
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Hamed Hassani
6985f52430e925bc620acf9a
Paul Bogdan
6985f52430e925bc620acf9b
Dawn Song
6985f52430e925bc620acf9c
Sharon Li
Towards Reducible Uncertainty Modeling for Reliable Large Language Model Agents
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.
Large language models require uncertainty quantification frameworks that account for interactive agent behavior rather than traditional single-turn question answering scenarios.
uncertainty quantification large language models interactive agents open world uncertainty accumulation process conditional uncertainty reduction process agent trajectory action interactivity