mlsanityData to 14 Oct 2026

Agentic Uncertainty Quantification

arXiv 2601.15703 ↗First posted 22 Jan 2026cs.AI cs.CLcited by 19

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

Abstract (arXiv)

Although AI agents have demonstrated impressive capabilities in long-horizon reasoning, their reliability is severely hampered by the ``Spiral of Hallucination,'' where early epistemic errors propagate irreversibly. Existing methods face a dilemma: uncertainty quantification (UQ) methods typically act as passive sensors, only diagnosing risks without addressing them, while self-reflection mechanisms suffer from continuous or aimless corrections. To bridge this gap, we propose a unified Dual-Process Agentic UQ (AUQ) framework that transforms verbalized uncertainty into active, bi-directional control signals. Our architecture comprises two complementary mechanisms: System 1 (Uncertainty-Aware Memory, UAM), which implicitly propagates verbalized confidence and semantic explanations to prevent blind decision-making; and System 2 (Uncertainty-Aware Reflection, UAR), which utilizes these explanations as rational cues to trigger targeted inference-time resolution only when necessary. This enables the agent to balance efficient execution and deep deliberation dynamically. Extensive experiments on closed-loop benchmarks and open-ended deep research tasks demonstrate that our training-free approach achieves superior performance and trajectory-level calibration. We believe this principled framework AUQ represents a significant step towards reliable agents.

Key facts. Cited by 19 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 27 of its 43 references that are in the map. First posted on arXiv 22 Jan 2026.

Builds on

27 of 43 references.

Closest ancestors · 3

Same direction, newest first

  1. 2025UProp: Investigating the Uncertainty Propagation of LLMs in Multi-Step Agentic Decision-Making20 Jun
  2. 2025Position: Uncertainty Quantification Needs Reassessment for Large-language Model Agents28 May
  3. 2025Tools in the Loop: Quantifying Uncertainty of LLM Question Answering Systems That Use Tools22 May

Other references · 24

  1. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  2. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,708
  3. 2017On Calibration of Modern Neural Networkscited by 4,960
  4. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,182
  5. 2023Lost in the Middle: How Language Models Use Long Contextscited by 3,455
  6. 2017What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?cited by 2,352
  7. 2024Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameterscited by 1,986
  8. 2022Language Models (Mostly) Know What They Knowcited by 1,856
  9. 2022WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agentscited by 1,251
  10. 2020ALFWorld: Aligning Text and Embodied Environments for Interactive Learningcited by 1,205
  11. 2023Large Language Models Cannot Self-Correct Reasoning Yetcited by 1,017
  12. 2023Just Ask for Calibration: Strategies for Eliciting Calibrated Confidence Scores from Language Models Fine-Tuned with Human Feedbackcited by 812
12 more
  1. 2023CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquingcited by 783
  2. 2022Teaching Models to Express Their Uncertainty in Wordscited by 751
  3. 2023Faith and Fate: Limits of Transformers on Compositionalitycited by 503
  4. 2022Self-critiquing models for assisting human evaluatorscited by 380
  5. 2023How Language Model Hallucinations Can Snowballcited by 311
  6. 2025DeepResearch Bench: A Comprehensive Benchmark for Deep Research Agentscited by 248
  7. 2025Where LLM Agents Fail and How They can Learn From Failurescited by 133
  8. 2025STeCa: Step-level Trajectory Calibration for LLM Agent Learningcited by 37
  9. 2020Modern Monte Carlo Methods for Efficient Uncertainty Quantification and Propagation: A Surveycited by 21
  10. 2025Enterprise Deep Research: Steerable Multi-Agent Deep Research for Enterprise Analyticscited by 20
  11. 2024Efficient Non-Parametric Uncertainty Quantification for Black-Box Large Language Models and Decision Planningcited by 16
  12. 2024(Implicit) Ensembles of Ensembles: Epistemic Uncertainty Collapse in Large Modelscited by 15

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 19.

19 papers.

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  1. 2026Uncertainty Quantification in LLM Agents: Foundations, Emerging Challenges, and Opportunitiescited by 20
  2. 2026Same Signal, Opposite Meaning: Direction-Informed Adaptive Learning for LLM Agentscited by 8
  3. 2026Old Habits Die Hard: How Conversational History Geometrically Traps LLMscited by 6
  4. 2026Neglected Free Lunch from Post-training: Progress Advantage for LLM Agentscited by 6
  5. 2026SafeCommit: Certifying When Memory-Grounded Agents May Safely Actcited by 6
  6. 2026Efficient Serving for Dynamic Agent Workflows with Prediction-based KV-Cache Managementcited by 4
  7. 2026When Agents Disagree With Themselves: Behavioral Consistency as an Uncertainty Signal for LLM Agentscited by 3
  8. 2026Oblivion: Self-Adaptive Agentic Memory Control through Decay-Driven Activationcited by 3
  9. 2026Uncertainty Decomposition for Clarification Seeking in LLM Agentscited by 3
  10. 2026Know When to Trust the Skill: Delayed Appraisal and Epistemic Vigilance for Single-Agent LLMscited by 0
  11. 2026CUSP: Decomposable Collective Uncertainty for Multi-Agent Multimodal Reasoningcited by 0
  12. 2026Uncertainty Quantification for LLM Agents: A Taxonomy, an Evaluation Protocol, and an Empirical Studycited by 0
7 more
  1. 2026Confidence Comes from Experience: Experiential Confidence Estimation from Reasoning to Agentscited by 0
  2. 2026GRUET: Quantifying Uncertainty of Agentic Reasoning-and-Acting Processescited by 0
  3. 2026CRC-Router: Risk-Constrained Routing for Medical Agentic AI Systemscited by 0
  4. 2026What Can Component-Replacement Evidence Establish? A Critical Scoping Review of Local Decisions in LLM Agentscited by 0
  5. 2026Prospective Hindsight: Self-Calibrating Reinforcement Learning via Prediction-Reality Gapscited by 0
  6. 2026From Uncertainty to Action: Learning to Steer LLM Agentscited by 0
  7. 2026Agentic Self-Awareness: Aligning Knowing, Acting, and Holdingcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Reasoning about Uncertainty: Do Reasoning Models Know When They Don't Know?
  2. 2026Proper Scoring Rules for Agentic Uncertainty Quantification
  3. 2026From Passive Metric to Active Signal: The Evolving Role of Uncertainty Quantification in Large Language Models
  4. 2024BayesAgent: Bayesian Agentic Reasoning Under Uncertainty via Verbalized Probabilistic Graphical Modeling
  5. 2024Towards Uncertainty-Aware Language Agent
  6. 2026The Cost of Reasoning: Chain-of-Thought Induces Overconfidence in Vision-Language Models
  7. 2026Beyond Single-Turn Confidence: Trajectory-Adapted Uncertainty Quantification for LLM Agents
  8. 2026Agentic Control in Variational Language Models