# Agentic Uncertainty Quantification

arXiv: https://arxiv.org/abs/2601.15703 · HTML: https://mlsanity.com/paper/2601.15703/

## 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.

## 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, 27 of 27)

- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://mlsanity.com/paper/2203.11171/) (2022)
- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Toolformer: Language Models Can Teach Themselves to Use Tools](https://mlsanity.com/paper/2302.04761/) (2023)
- [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023)
- [What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?](https://mlsanity.com/paper/1703.04977/) (2017)
- [Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters](https://mlsanity.com/paper/2408.03314/) (2024)
- [Language Models (Mostly) Know What They Know](https://mlsanity.com/paper/2207.05221/) (2022)
- [WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents](https://mlsanity.com/paper/2207.01206/) (2022)
- [ALFWorld: Aligning Text and Embodied Environments for Interactive Learning](https://mlsanity.com/paper/2010.03768/) (2020)
- [Large Language Models Cannot Self-Correct Reasoning Yet](https://mlsanity.com/paper/2310.01798/) (2023)
- [Just Ask for Calibration: Strategies for Eliciting Calibrated Confidence Scores from Language Models Fine-Tuned with Human Feedback](https://mlsanity.com/paper/2305.14975/) (2023)
- [CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing](https://mlsanity.com/paper/2305.11738/) (2023)
- [Teaching Models to Express Their Uncertainty in Words](https://mlsanity.com/paper/2205.14334/) (2022)
- [Faith and Fate: Limits of Transformers on Compositionality](https://mlsanity.com/paper/2305.18654/) (2023)
- [Self-critiquing models for assisting human evaluators](https://mlsanity.com/paper/2206.05802/) (2022)
- [How Language Model Hallucinations Can Snowball](https://mlsanity.com/paper/2305.13534/) (2023)
- [DeepResearch Bench: A Comprehensive Benchmark for Deep Research Agents](https://mlsanity.com/paper/2506.11763/) (2025)
- [Where LLM Agents Fail and How They can Learn From Failures](https://mlsanity.com/paper/2509.25370/) (2025)
- [UProp: Investigating the Uncertainty Propagation of LLMs in Multi-Step Agentic Decision-Making](https://mlsanity.com/paper/2506.17419/) (2025)
- [STeCa: Step-level Trajectory Calibration for LLM Agent Learning](https://mlsanity.com/paper/2502.14276/) (2025)
- [Position: Uncertainty Quantification Needs Reassessment for Large-language Model Agents](https://mlsanity.com/paper/2505.22655/) (2025)
- [Modern Monte Carlo Methods for Efficient Uncertainty Quantification and Propagation: A Survey](https://mlsanity.com/paper/2011.00680/) (2020)
- [Enterprise Deep Research: Steerable Multi-Agent Deep Research for Enterprise Analytics](https://mlsanity.com/paper/2510.17797/) (2025)
- [Efficient Non-Parametric Uncertainty Quantification for Black-Box Large Language Models and Decision Planning](https://mlsanity.com/paper/2402.00251/) (2024)
- [(Implicit) Ensembles of Ensembles: Epistemic Uncertainty Collapse in Large Models](https://mlsanity.com/paper/2409.02628/) (2024)
- [Tools in the Loop: Quantifying Uncertainty of LLM Question Answering Systems That Use Tools](https://mlsanity.com/paper/2505.16113/) (2025)

## Built on by (most cited first, 19 of 19)

- [Uncertainty Quantification in LLM Agents: Foundations, Emerging Challenges, and Opportunities](https://mlsanity.com/paper/2602.05073/) (2026)
- [Same Signal, Opposite Meaning: Direction-Informed Adaptive Learning for LLM Agents](https://mlsanity.com/paper/2605.06908/) (2026)
- [Old Habits Die Hard: How Conversational History Geometrically Traps LLMs](https://mlsanity.com/paper/2603.03308/) (2026)
- [Neglected Free Lunch from Post-training: Progress Advantage for LLM Agents](https://mlsanity.com/paper/2606.26080/) (2026)
- [SafeCommit: Certifying When Memory-Grounded Agents May Safely Act](https://mlsanity.com/paper/2608.04289/) (2026)
- [Efficient Serving for Dynamic Agent Workflows with Prediction-based KV-Cache Management](https://mlsanity.com/paper/2605.06472/) (2026)
- [When Agents Disagree With Themselves: Behavioral Consistency as an Uncertainty Signal for LLM Agents](https://mlsanity.com/paper/2602.11619/) (2026)
- [Oblivion: Self-Adaptive Agentic Memory Control through Decay-Driven Activation](https://mlsanity.com/paper/2604.00131/) (2026)
- [Uncertainty Decomposition for Clarification Seeking in LLM Agents](https://mlsanity.com/paper/2606.19559/) (2026)
- [Know When to Trust the Skill: Delayed Appraisal and Epistemic Vigilance for Single-Agent LLMs](https://mlsanity.com/paper/2604.16753/) (2026)
- [CUSP: Decomposable Collective Uncertainty for Multi-Agent Multimodal Reasoning](https://mlsanity.com/paper/2609.05708/) (2026)
- [Uncertainty Quantification for LLM Agents: A Taxonomy, an Evaluation Protocol, and an Empirical Study](https://mlsanity.com/paper/2609.07395/) (2026)
- [Confidence Comes from Experience: Experiential Confidence Estimation from Reasoning to Agents](https://mlsanity.com/paper/2609.17708/) (2026)
- [GRUET: Quantifying Uncertainty of Agentic Reasoning-and-Acting Processes](https://mlsanity.com/paper/2609.24831/) (2026)
- [CRC-Router: Risk-Constrained Routing for Medical Agentic AI Systems](https://mlsanity.com/paper/2609.30714/) (2026)
- [What Can Component-Replacement Evidence Establish? A Critical Scoping Review of Local Decisions in LLM Agents](https://mlsanity.com/paper/2609.39989/) (2026)
- [Prospective Hindsight: Self-Calibrating Reinforcement Learning via Prediction-Reality Gaps](https://mlsanity.com/paper/2610.02740/) (2026)
- [From Uncertainty to Action: Learning to Steer LLM Agents](https://mlsanity.com/paper/2610.09115/) (2026)
- [Agentic Self-Awareness: Aligning Knowing, Acting, and Holding](https://mlsanity.com/paper/2610.19226/) (2026)

## 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.