Uncertainty Decomposition improves clarification F1 in LLMs

Gregory Matsnev submitted a paper on 17 Jun 2026 proposing a prompt-based uncertainty decomposition that separates action confidence from "request uncertainty (u)" so agents ask for clarification when task specifications are ambiguous. The method is evaluated across five LLM backbones and two new clarification-augmented benchmarks, with concrete gains reported in clarification F1.

What did the paper introduce?

The paper introduces a prompt-based decomposition that separates action confidence from "request uncertainty (u)" and positions prompt-based estimation as the most viable deployment-time approach under realistic constraints. The author argues classical aleatoric/epistemic splits fall short for interactive LLM agents and that black-box APIs, interactive latency budgets, and the absence of labeled trajectories rule out logprob-based, multi-sampling, and training-based methods, leaving prompt-based estimation as the practical alternative.

Matsnev frames the decomposition as a way to make uncertainty communicable to users and to let agents proactively ask clarification questions when the task specification is underspecified. The manuscript is 26 pages long and includes 8 figures, and the paper provides source code via a linked URL.

How was the method evaluated and what were the results?

The evaluation uses two clarification-augmented benchmarks, WebShop-Clarification and ALFWorld-Clarification, in which 50% of tasks are deliberately underspecified, and compares the proposed decomposition against ReAct+UE and Uncertainty-Aware Memory (UAM) across five LLM backbones. The five backbones evaluated are GPT-5.1, DeepSeek-v3.2-exp, GLM-4.7, Qwen3.5-35B, and GPT-OSS-120B, and the experiments also include the standard WebShop, ALFWorld, and REAL benchmarks for fault detection.

Averaged across the five backbones, the proposed decomposition improves clarification F1 on ALFWorld-Clarification by 73% over ReAct+UE and by 36% over UAM. The decomposition also leads clarification F1 on every backbone on WebShop-Clarification and on four of five backbones on ALFWorld-Clarification, indicating the gains generalize beyond a single model.

Why it matters

Prompt-based uncertainty signals can be computed at deployment without access to model internals or additional training, which matters for real-world systems that use black-box LLM APIs and have tight latency constraints. By separating an agent's action confidence from a distinct request uncertainty signal, agents can choose to act when the spec is clear and ask targeted clarification when it is not, potentially reducing failure modes caused by underspecification.

The concrete improvements in clarification F1, notably the 73% average gain over ReAct+UE on ALFWorld-Clarification, show that this is not merely a theoretical decomposition but a practical intervention that changes agent behavior across multiple LLMs.

What to watch

Check the linked source code provided with the paper to reproduce the prompt-based decomposition and the two clarification-augmented benchmarks. Also watch whether the approach replicates on additional benchmarks and models beyond the five backbones evaluated, and whether follow-up work adapts the decomposition to settings with labeled trajectories or different latency budgets.

References: the paper (arXiv:2606.19559) was submitted on 17 Jun 2026 and includes the two new benchmarks WebShop-Clarification and ALFWorld-Clarification, the five listed backbones, and the reported average improvements in clarification F1.