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Uncertainty Decomposition for Clarification Seeking in LLM Agents

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arXiv:2606. 19559v1 Announce Type: new Abstract: Recent position papers argue that the classical aleatoric/epistemic uncertainty framework is insufficient for interactive large language model (LLM) agents and call for underspecification-aware, decomposed, and communicable uncertainty representations that can unlock new agent capabilities such as proactive clarification seeking and shared mental-model building.

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Authors: Gregory Matsnev
Released: 2026-06-20 (19d ago)
Topic: Related to current project
Relation to my work: Read next because Uncertainty Decomposition for Clarification Seeking in LLM Agents overlaps with clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)", clean result "Only continuous soft prefixes hit both EM axes at once on Qwen-2.5-7B-Instruct: discrete prompt searches split between the alignment objective and the distributional objective, and both discretizations of the soft prefix collapse (MODERATE confidence)". Matching terms: class, under, eval, rate, compare, position, language, model. Source: arxiv cs.AI (Artificial Intelligence).
arXiv: 2606.19559

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