mlsanityData to 14 Oct 2026

Uncertainty-Aware Clarification in LLM Agents with Information Gain

arXiv 2606.03135 ↗First posted 2 Jun 2026cs.AIcited by 8

On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks

Abstract (arXiv)

Large Language Model (LLM) agents often operate under underspecified user instructions, where latent uncertainty over user intent leads to erroneous tool actions. To address this challenge, we propose a goal-oriented clarification framework that aligns clarification behavior with ambiguity resolution. Central to our approach is the Information Gain Reward, a metric that quantifies the utility of clarification questions by measuring the Bayesian belief update towards the ground-truth goal induced by the clarification exchange. We train the clarifier (LLM) using this reward to optimize for high information gain, ensuring that clarifications effectively reduce uncertainty and improve task completion within the agent-tool-user environment. We validate our framework within a clarification-enhanced τ-Bench environment, conducting cross-agent evaluations across five heterogeneous backbones. Empirical results demonstrate that our method consistently improves the success rate by 3.7% over the no-clarification baseline, while adding only 0.3 total interaction steps on average.

Key facts. Cited by 8 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 35 of its 44 references that are in the map. First posted on arXiv 2 Jun 2026.

Builds on

35 of 44 references.

Closest ancestors · 11

Same direction, newest first

  1. 2025Structured Uncertainty guided Clarification for LLM Agents11 Nov
  2. 2025AGENTIF: Benchmarking Instruction Following of Large Language Models in Agentic Scenarios22 May
  3. 2025Survey on Evaluation of LLM-based Agents20 Mar
  4. 2025Agentic Reasoning: A Streamlined Framework for Enhancing LLM Reasoning with Agentic Tools7 Feb
  5. 2024TheAgentCompany: Benchmarking LLM Agents on Consequential Real World Tasks18 Dec
  6. 2024GTA: A Benchmark for General Tool Agents11 Jul
  7. 2024Ask-before-Plan: Proactive Language Agents for Real-World Planning18 Jun
  8. 2024τ-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains17 Jun
  9. 2024AvaTaR: Optimizing LLM Agents for Tool Usage via Contrastive Reasoning17 Jun
  10. 2024StreamBench: Towards Benchmarking Continuous Improvement of Language Agents13 Jun
  11. 2023AgentBench: Evaluating LLMs as Agents7 Aug

Other references · 24

  1. 2022Training language models to follow instructions with human feedbackcited by 15,971
  2. 2024DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Modelscited by 9,379
  3. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  4. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,182
  5. 2025DAPO: An Open-Source LLM Reinforcement Learning System at Scalecited by 2,810
  6. 2024HybridFlow: A Flexible and Efficient RLHF Frameworkcited by 2,057
  7. 2023Instruction Tuning for Large Language Models: A Surveycited by 623
  8. 2024AgentHarm: A Benchmark for Measuring Harmfulness of LLM Agentscited by 437
  9. 2024ToolSandbox: A Stateful, Conversational, Interactive Evaluation Benchmark for LLM Tool Use Capabilitiescited by 295
  10. 2024Tool Learning with Large Language Models: A Surveycited by 223
  11. 2024EASYTOOL: Enhancing LLM-based Agents with Concise Tool Instructioncited by 154
  12. 2024Embodied Agent Interface: Benchmarking LLMs for Embodied Decision Makingcited by 144
12 more
  1. 2019Asking Clarifying Questions in Open-Domain Information-Seeking Conversationscited by 139
  2. 2018Learning to Ask Good Questions: Ranking Clarification Questions using Neural Expected Value of Perfect Informationcited by 115
  3. 2024Limitations of the LLM-as-a-Judge Approach for Evaluating LLM Outputs in Expert Knowledge Taskscited by 106
  4. 2024RLHF Deciphered: A Critical Analysis of Reinforcement Learning from Human Feedback for LLMscited by 90
  5. 2025A Survey on Large Language Models for Mathematical Reasoningcited by 52
  6. 2025LLM Agents Making Agent Toolscited by 48
  7. 2020A Survey on Complex Question Answering over Knowledge Base: Recent Advances and Challengescited by 47
  8. 2024LLM-as-a-Judge & Reward Model: What They Can and Cannot Docited by 32
  9. 2025BED-LLM: Intelligent Information Gathering with LLMs and Bayesian Experimental Designcited by 24
  10. 2025From Passive to Active Reasoning: Can Large Language Models Ask the Right Questions under Incomplete Information?cited by 23
  11. 2024CLARINET: Augmenting Language Models to Ask Clarification Questions for Retrievalcited by 20
  12. 2025From Selection to Generation: A Survey of LLM-based Active Learningcited by 19

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 8.

8 papers.

  1. 2026Safeguards Based on Copyable Context Cannot Provide Reliable Safety for LLMscited by 1
  2. 2026Hypotheses-Guided Self Distillation for Continual Personalizationcited by 1
  3. 2026Uncertainty Quantification for LLM Agents: A Taxonomy, an Evaluation Protocol, and an Empirical Studycited by 0
  4. 2026RESKILL: Explicit Failure Attribution and Structured Repair for Interactive Language Agentscited by 0
  5. 2026Clarify the User or Verify the World? Uncertainty Routing for Proactive Agentscited by 0
  6. 2026What Can Component-Replacement Evidence Establish? A Critical Scoping Review of Local Decisions in LLM Agentscited by 0
  7. 2026SynAct: Synergistic Active Feedback Learning for LLM Agentscited by 0
  8. 2026CLEAT: Learning Value-Guided Clarification for Interactive Agentscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Uncertainty Decomposition for Clarification Seeking in LLM Agents
  2. 2026Ask or Assume? Uncertainty-Aware Clarification-Seeking in Coding Agents
  3. 2025ClarifyMT-Bench: Benchmarking and Improving Multi-Turn Clarification for Conversational Large Language Models
  4. 2026CAR-bench: Evaluating the Consistency and Limit-Awareness of LLM Agents under Real-World Uncertainty
  5. 2026Uncertainty Quantification in LLM Agents: Foundations, Emerging Challenges, and Opportunities
  6. 2025Position: Uncertainty Quantification Needs Reassessment for Large-language Model Agents
  7. 2025Can Code Language Models Learn Clarification-Seeking Behaviors?
  8. 2026A Tri-Agent Framework for Evaluating and Aligning Question Clarification Capabilities of Large Language Models