# Structured Uncertainty guided Clarification for LLM Agents

arXiv: https://arxiv.org/abs/2511.08798 · HTML: https://mlsanity.com/paper/2511.08798/

## Key facts

- Cited by 41 papers in the map (data through 14 Oct 2026).
- Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use.
- Builds on 20 of its 54 references that are in the map.
- First posted on arXiv 11 Nov 2025.

## Placed in

- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)

## Builds on (most cited first, 20 of 20)

- [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://mlsanity.com/paper/2402.03300/) (2024)
- [Toolformer: Language Models Can Teach Themselves to Use Tools](https://mlsanity.com/paper/2302.04761/) (2023)
- [τ-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains](https://mlsanity.com/paper/2406.12045/) (2024)
- [Understanding the planning of LLM agents: A survey](https://mlsanity.com/paper/2402.02716/) (2024)
- [TravelPlanner: A Benchmark for Real-World Planning with Language Agents](https://mlsanity.com/paper/2402.01622/) (2024)
- [AgentBoard: An Analytical Evaluation Board of Multi-turn LLM Agents](https://mlsanity.com/paper/2401.13178/) (2024)
- [FinMem: A Performance-Enhanced LLM Trading Agent with Layered Memory and Character Design](https://mlsanity.com/paper/2311.13743/) (2023)
- [CollabLLM: From Passive Responders to Active Collaborators](https://mlsanity.com/paper/2502.00640/) (2025)
- [Prompting and Evaluating Large Language Models for Proactive Dialogues: Clarification, Target-guided, and Non-collaboration](https://mlsanity.com/paper/2305.13626/) (2023)
- [Learning to Ask Good Questions: Ranking Clarification Questions using Neural Expected Value of Perfect Information](https://mlsanity.com/paper/1805.04655/) (2018)
- [Clarify When Necessary: Resolving Ambiguity Through Interaction with LMs](https://mlsanity.com/paper/2311.09469/) (2023)
- [Modeling Future Conversation Turns to Teach LLMs to Ask Clarifying Questions](https://mlsanity.com/paper/2410.13788/) (2024)
- [CRMArena: Understanding the Capacity of LLM Agents to Perform Professional CRM Tasks in Realistic Environments](https://mlsanity.com/paper/2411.02305/) (2024)
- [MLLM-Tool: A Multimodal Large Language Model For Tool Agent Learning](https://mlsanity.com/paper/2401.10727/) (2024)
- [Ask-before-Plan: Proactive Language Agents for Real-World Planning](https://mlsanity.com/paper/2406.12639/) (2024)
- [Learning to Clarify: Multi-turn Conversations with Action-Based Contrastive Self-Training](https://mlsanity.com/paper/2406.00222/) (2024)
- [PACIFIC: Towards Proactive Conversational Question Answering over Tabular and Textual Data in Finance](https://mlsanity.com/paper/2210.08817/) (2022)
- [When2Call: When (not) to Call Tools](https://mlsanity.com/paper/2504.18851/) (2025)
- [MMAU: A Holistic Benchmark of Agent Capabilities Across Diverse Domains](https://mlsanity.com/paper/2407.18961/) (2024)
- [A Framework for Testing and Adapting REST APIs as LLM Tools](https://mlsanity.com/paper/2504.15546/) (2025)

## Built on by (most cited first, 30 of 41)

- [HiL-Bench (Human-in-Loop Benchmark): Do Agents Know When to Ask for Help?](https://mlsanity.com/paper/2604.09408/) (2026)
- [Uncertainty Quantification in LLM Agents: Foundations, Emerging Challenges, and Opportunities](https://mlsanity.com/paper/2602.05073/) (2026)
- [Toward Personalized LLM-Powered Agents: Foundations, Evaluation, and Future Directions](https://mlsanity.com/paper/2602.22680/) (2026)
- [SELAUR: Self Evolving LLM Agent via Uncertainty-aware Rewards](https://mlsanity.com/paper/2602.21158/) (2026)
- [Uncertainty-Aware Clarification in LLM Agents with Information Gain](https://mlsanity.com/paper/2606.03135/) (2026)
- [LHAW: Controllable Underspecification for Long-Horizon Tasks](https://mlsanity.com/paper/2602.10525/) (2026)
- [SafeCommit: Certifying When Memory-Grounded Agents May Safely Act](https://mlsanity.com/paper/2608.04289/) (2026)
- [Ambig-DS: A Benchmark for Task-Framing Ambiguity in Data-Science Agents](https://mlsanity.com/paper/2605.09698/) (2026)
- [Asking What Matters: Reward-Driven Clarification for Software Engineering Tasks](https://mlsanity.com/paper/2604.14624/) (2026)
- [Ask Early, Ask Late, Ask Right: When Does Clarification Timing Matter for Long-Horizon Agents?](https://mlsanity.com/paper/2605.07937/) (2026)
- [Exploring Agentic Tool-Calling Decisions via Uncertainty-Aligned Reinforcement Learning](https://mlsanity.com/paper/2606.06976/) (2026)
- [Uncertainty Propagation in LLM-Based Systems](https://mlsanity.com/paper/2604.23505/) (2026)
- [Position: agentic AI orchestration should be Bayes-consistent](https://mlsanity.com/paper/2605.00742/) (2026)
- [Agentic Coding Needs Proactivity, Not Just Autonomy](https://mlsanity.com/paper/2605.06717/) (2026)
- [Uncertainty Decomposition for Clarification Seeking in LLM Agents](https://mlsanity.com/paper/2606.19559/) (2026)
- [AgentGym2: Benchmarking Large Language Model Agents in De-Idealized Real-World Environments](https://mlsanity.com/paper/2607.05174/) (2026)
- [Frictive Policy Optimization for LLMs: Epistemic Intervention, Risk-Sensitive Control, and Reflective Alignment](https://mlsanity.com/paper/2604.25136/) (2026)
- [Actionable Hallucination Detection: Translating Latent Uncertainty into Agentic Critique](https://mlsanity.com/paper/2608.10430/) (2026)
- [ASPI: Seeking Ambiguity Clarification Amplifies Prompt Injection Vulnerability in LLM Agents](https://mlsanity.com/paper/2605.17324/) (2026)
- [The Architecture of Errors: From Universal Impossibility to Patch-Local LLM Reliability](https://mlsanity.com/paper/2605.30628/) (2026)
- [Bayesian control for coding agents](https://mlsanity.com/paper/2606.24453/) (2026)
- [SAFETY SENTRY: Context-Aware Human Intervention via EXECUTE-ASK-REFUSE Routing](https://mlsanity.com/paper/2607.13594/) (2026)
- [Beyond Single-Turn Confidence: Trajectory-Adapted Uncertainty Quantification for LLM Agents](https://mlsanity.com/paper/2608.11552/) (2026)
- [AcCoRD: Evaluating User-Agent Collaboration Under Realistic User Preference Dynamics](https://mlsanity.com/paper/2608.27818/) (2026)
- [Thinking Is Not Telling: Information Disclosure in User-Service LLM Agents](https://mlsanity.com/paper/2602.07796/) (2026)
- [Don't Start What You Can't Finish: A Counterfactual Audit of Support-State Triage in LLM Agents](https://mlsanity.com/paper/2604.16752/) (2026)
- [Communication Policy Evolution for Proactive LLM Agents](https://mlsanity.com/paper/2606.14314/) (2026)
- [LOGOS: A Living Logic for AI Agent Teams That Evolve With Humans](https://mlsanity.com/paper/2607.10878/) (2026)
- [Intent-Driven Situation Tracking for User-Centric Multi-Turn Agents](https://mlsanity.com/paper/2608.15755/) (2026)
- [Ask Before You Optimize: Dynamic Pre-Formulation Clarification for Interactive Optimization](https://mlsanity.com/paper/2609.05258/) (2026)

## Abstract (arXiv)

LLM agents with tool-calling capabilities often fail when user instructions are ambiguous or incomplete, leading to incorrect invocations and task failures. Existing approaches operate in unstructured language spaces, generating clarifying questions through prompting strategies that lack principled criteria for determining which questions to ask and when to stop. We introduce a principled formulation of structured uncertainty that operates directly over tool parameters and their domains, cleanly separating specification uncertainty (what the user wants) from model uncertainty (what the LLM predicts). Our formulation uses Expected Value of Perfect Information (EVPI) to quantify the disambiguation value of each potential question, balanced against aspect-based cost modeling that prevents redundant questioning. We demonstrate the versatility of this formulation through two applications. First, SAGE-Agent uses structured uncertainty for inference-time question selection, achieving 7-39% higher coverage on ambiguous tasks while reducing clarification questions by 1.5-2.7x compared to strong prompting and uncertainty-based baselines. Second, we show that structured uncertainty provides effective training signals: uncertainty-guided reward modeling boosts When2Call accuracy from 36.5% to 65.2% (3B model) and 36.7% to 62.9% (7B model) through uncertainty-weighted GRPO training, demonstrating more sample-efficient reinforcement learning for tool-calling agents. To enable evaluation, we present ClarifyBench, the first multi-turn dynamic tool-calling disambiguation benchmark. Our results establish structured uncertainty as a principled framework that improves both inference-time interaction efficiency and training-time sample efficiency in tool-augmented agents.