mlsanityData to 7 Oct 2026

Uncertainty Quantification in LLM Agents: Foundations, Emerging Challenges, and Opportunities

arXiv 2602.05073 ↗First posted 4 Feb 2026cs.AIcited by 15

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

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Abstract (arXiv)

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 three pillars to build a solid ground for future agent UQ research: (1. Foundations) We present the first general formulation of agent UQ that subsumes broad classes of existing UQ setups; (2. Challenges) We identify four technical challenges specifically tied to agentic setups -- selection of uncertainty estimator, uncertainty of heterogeneous entities, modeling uncertainty dynamics in interactive systems, and lack of fine-grained benchmarks -- with numerical analysis on a real-world agent benchmark, τ2-bench; (3. Future Directions) We conclude with noting on the practical implications of agent UQ and remaining open problems as forward-looking discussion for future explorations.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 156 of its 213 references that are in the map. First posted on arXiv 4 Feb 2026.

Builds on

156 of 213 references.

Closest ancestors · 20

Same direction, newest first

  1. 2026Agentic Uncertainty Quantification22 Jan
  2. 2025Adaptation of Agentic AI: A Survey of Post-Training, Memory, and Skills18 Dec
  3. 2025Structured Uncertainty guided Clarification for LLM Agents11 Nov
  4. 2025The Tool Decathlon: Benchmarking Language Agents for Diverse, Realistic, and Long-Horizon Task Execution29 Oct
  5. 2025GAP: Graph-Based Agent Planning with Parallel Tool Use and Reinforcement Learning29 Oct
  6. 2025UProp: Investigating the Uncertainty Propagation of LLMs in Multi-Step Agentic Decision-Making20 Jun
  7. 2025Flex-TravelPlanner: A Benchmark for Flexible Planning with Language Agents5 Jun
  8. 2025Position: Uncertainty Quantification Needs Reassessment for Large-language Model Agents28 May
  9. 2025Vending-Bench: A Benchmark for Long-Term Coherence of Autonomous Agents20 Feb
  10. 2024TheAgentCompany: Benchmarking LLM Agents on Consequential Real World Tasks18 Dec
  11. 2024Rethinking LLM Uncertainty: A Multi-Agent Approach to Estimating Black-Box Model Uncertainty12 Dec
  12. 2024Agent-as-a-Judge: Evaluate Agents with Agents14 Oct
8 more
  1. 2024τ-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains17 Jun
  2. 2024Confidence Calibration and Rationalization for LLMs via Multi-Agent Deliberation14 Apr
  3. 2024TravelPlanner: A Benchmark for Real-World Planning with Language Agents2 Feb
  4. 2024Towards Uncertainty-Aware Language Agent25 Jan
  5. 2024AgentBoard: An Analytical Evaluation Board of Multi-turn LLM Agents24 Jan
  6. 2024Large Language Model based Multi-Agents: A Survey of Progress and Challenges21 Jan
  7. 2023SOTOPIA: Interactive Evaluation for Social Intelligence in Language Agents18 Oct
  8. 2023AgentBench: Evaluating LLMs as Agents7 Aug

Other references · 136

  1. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  2. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
  3. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,601
  4. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,416
  5. 2023Reflexion: Language Agents with Verbal Reinforcement Learningcited by 4,064
  6. 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,686
  7. 2023Let's Verify Step by Stepcited by 3,598
  8. 2023Self-Refine: Iterative Refinement with Self-Feedbackcited by 3,317
  9. 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,193
  10. 2022Constitutional AI: Harmlessness from AI Feedbackcited by 2,950
  11. 2022Do As I Can, Not As I Say: Grounding Language in Robotic Affordancescited by 2,302
  12. 2023Voyager: An Open-Ended Embodied Agent with Large Language Modelscited by 2,081
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  1. 2023WebArena: A Realistic Web Environment for Building Autonomous Agentscited by 1,908
  2. 2023AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversationcited by 1,906
  3. 2023ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIscited by 1,880
  4. 2022Language Models (Mostly) Know What They Knowcited by 1,783
  5. 2023Improving Factuality and Reasoning in Language Models through Multiagent Debatecited by 1,739
  6. 2025s1: Simple test-time scalingcited by 1,311
  7. 2023Mind2Web: Towards a Generalist Agent for the Webcited by 1,210
  8. 2022WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agentscited by 1,197
  9. 2020ALFWorld: Aligning Text and Embodied Environments for Interactive Learningcited by 1,143
  10. 2024OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environmentscited by 1,134
  11. 2023GAIA: a benchmark for General AI Assistantscited by 1,084
  12. 2025A-MEM: Agentic Memory for LLM Agentscited by 1,000
  13. 2023Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debatecited by 995
  14. 2023Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMscited by 946
  15. 2022Solving math word problems with process- and outcome-based feedbackcited by 871
  16. 2023Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generationcited by 795
  17. 2023Just Ask for Calibration: Strategies for Eliciting Calibrated Confidence Scores from Language Models Fine-Tuned with Human Feedbackcited by 788
  18. 2023SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Modelscited by 787
  19. 2022Teaching Models to Express Their Uncertainty in Wordscited by 731
  20. 2018MINE: Mutual Information Neural Estimationcited by 680
  21. 2025BrowseComp: A Simple Yet Challenging Benchmark for Browsing Agentscited by 616
  22. 2023API-Bank: A Comprehensive Benchmark for Tool-Augmented LLMscited by 574
  23. 2025τ2-Bench: Evaluating Conversational Agents in a Dual-Control Environmentcited by 558
  24. 2025Beyond the 80/20 Rule: High-Entropy Minority Tokens Drive Effective Reinforcement Learning for LLM Reasoningcited by 540
  25. 2024Executable Code Actions Elicit Better LLM Agentscited by 537
  26. 2026Terminal-Bench: Benchmarking Agents on Hard, Realistic Tasks in Command Line Interfacescited by 515
  27. 2022LLM-Planner: Few-Shot Grounded Planning for Embodied Agents with Large Language Modelscited by 459
  28. 2025Stop Overthinking: A Survey on Efficient Reasoning for Large Language Modelscited by 456
  29. 2026Kimi K2.5: Visual Agentic Intelligencecited by 449
  30. 2024AgentHarm: A Benchmark for Measuring Harmfulness of LLM Agentscited by 406
  31. 2020AmbigQA: Answering Ambiguous Open-domain Questionscited by 385
  32. 2024AppWorld: A Controllable World of Apps and People for Benchmarking Interactive Coding Agentscited by 359
  33. 2024DINO-WM: World Models on Pre-trained Visual Features enable Zero-shot Planningcited by 357
  34. 2024WorkArena: How Capable Are Web Agents at Solving Common Knowledge Work Tasks?cited by 348
  35. 2023MLAgentBench: Evaluating Language Agents on Machine Learning Experimentationcited by 339
  36. 2023MINT: Evaluating LLMs in Multi-turn Interaction with Tools and Language Feedbackcited by 293
  37. 2025PaperBench: Evaluating AI's Ability to Replicate AI Researchcited by 286
  38. 2024Agent-SafetyBench: Evaluating the Safety of LLM Agentscited by 280

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Citing papers per year: 2026: 15.

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  1. 2026Agentic Abstention: Do Agents Know When to Stop Instead of Act?cited by 9
  2. 2026Hide-and-Seek in Trajectories: Discovering Failure Signals for VLA Runtime Monitoringcited by 6
  3. 2026Uncertainty Decomposition for Clarification Seeking in LLM Agentscited by 3
  4. 2026When Agents Disagree With Themselves: Behavioral Consistency as an Uncertainty Signal for LLM Agentscited by 2
  5. 2026A Systematic Evaluation of Black-Box Uncertainty Estimation Methods for Large Language Modelscited by 2
  6. 2026Neglected Free Lunch from Post-training: Progress Advantage for LLM Agentscited by 2
  7. 2026Last Step Matters: Early Uncertainty Cannot Predict Failure in Long-Horizon Agentscited by 2
  8. 2026Rewarding Beliefs, Not Actions: Consistency-Guided Credit Assignment for Long-Horizon Agentscited by 1
  9. 2026Confidence Laundering in Agent Systems: Why Uncertainty Needs a Latent Carriercited by 1
  10. 2026CoMet: Context and Multiplicity Decomposition for Multimodal Uncertainty Estimationcited by 1
  11. 2026Beyond Single-Turn Confidence: Trajectory-Adapted Uncertainty Quantification for LLM Agentscited by 0
  12. 2026From Sequence to Structure: Relational Uncertainty Propagation for LLM Agentscited by 0
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  1. 2026Do Agents Know When They Succeed? Calibrating Agent Confidence from Internal Representationscited by 0
  2. 2026Epi-Logic: A Conceptual Framework for Epistemic Runtime Control, Schema Validity Checking, and Controlled Accommodation in Autonomous AI Agentscited by 0
  3. 2026GRUET: Quantifying Uncertainty of Agentic Reasoning-and-Acting Processescited by 0

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  1. 2026Uncertainty Quantification for LLM Agents: A Taxonomy, an Evaluation Protocol, and an Empirical Study
  2. 2025Uncertainty Quantification and Confidence Calibration in Large Language Models: A Survey
  3. 2026Estimating Uncertainty from Reasoning: A Large-Scale Study of Multi- and Crosslingual MCQA Performance in LLMs
  4. 2024Rethinking the Uncertainty: A Critical Review and Analysis in the Era of Large Language Models
  5. 2024UBench: Benchmarking Uncertainty in Large Language Models with Multiple Choice Questions
  6. 2026The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models
  7. 2026DiscoUQ: Structured Disagreement Analysis for Uncertainty Quantification in LLM Agent Ensembles
  8. 2026Uncertainty-Aware Clarification in LLM Agents with Information Gain