LLM Uncertainty Estimation

LLM: Large Language Model

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38 papers in the last four weeks, up 111% on the four weeks before. 0.4% of all new papers.

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When LLMs Agree, Are They Right? Auditing Self-Consistency and Cross-Model Agreement as Confidence Signals
Jul 9, 2026Kaihua DingLLM-as-a-Judge LLM Reliability
University of Pennsylvania
Can We Trust LLM's Logic? Quantifying Uncertainty, Coherence, and Robustness via a Graph-Based Framework
Jul 9, 2026Riccardo Revalor, Jalees Rehman, Debjit PalCoT Faithfulness LLM Reasoning with Graphs
¶Department of Electrical and Computer Engineering · ‡Department of Biochemistry and Molecular Genetics
Future Confidence Distillation in Large Language Models
Jul 8, 2026Sahil KaleConfidence Estimation in Language Models Language Model Calibration
University of California, Los Angeles Los Angeles, USA
What Predicts Correctness in Text-to-SQL? A Selective-Prediction Study
Jul 7, 2026Robert RichardsonLLM Answer Verification Selective Prediction
Affiliation
Estimating Uncertainty from Reasoning: A Large-Scale Study of Multi- and Crosslingual MCQA Performance in LLMs
Jul 7, 2026Andrea Bacciu, Andrea Alfarano, Saab Mansour +2Multilingual Language Model Evaluation Selective Prediction
Amazon · INSAIT, Sofia
Propose and Attend: Training-free MLLM Grounding Confidence via Multi-Token Localized Attention
Jul 7, 2026Daniel Shalam, Emanuel Ben Baruch, Avi Ben Cohen +1Multimodal Grounding LLM Uncertainty Estimation
Amazon
SpanUQ: Span-Level Uncertainty Quantification for Large Language Model Generation
Jul 7, 2026Yimeng Zhang, Yingying Zhuang, Ziyi Wang +12LLM Evaluation Confidence Estimation in Language Models
Amazon · Northeastern University
Uncertainty-Aware Abstention in Large Language Models with Provable Alignment Guarantees
Jul 5, 2026Sijin Dong, Hiroyuki ShinnouSelective Prediction Confidence Estimation in Language Models
1Ibaraki University, Japan.
The "I Don't Know" Filter: Enhancing Agentic Reliability in Function Calling
Jul 4, 2026Stefan Broecker, Mason del Rosario, Boris Selitser +1AI Agent Reliability LLM Agent Evaluation
University of California, Davis, Department of Computer Science · Okareo, Inc. · University of California, Davis, Department of Mathematics
Evaluating LLM Uncertainty in Long-Form Generation Using Deterministic Ground Truth
Jul 4, 2026Ido Amit, Ido Galil, Ran El-YanivLLM Evaluation LLM Uncertainty Estimation
1Technion · 2Nvidia
Bayesian Sparse Low-Rank Adaptation for Large Language Model Uncertainty Estimation
Jul 2, 2026Jijie Zhang, Zhe Ren, Quan Zhang +1Low-Rank Adaptation Language Model Calibration
School of Artificial Intelligence, Jilin University · Michigan State University
Bayesian Uncertainty Propagation for Agentic RAG Pipelines: A Proof-of-Concept Study on Multi-Hop Question Answering
Jul 1, 2026Louis Donaldson, Connor Walker, Koorosh Aslansefat +1Uncertainty Quantification Agentic RAG
University of Hull, Hull HU6 7RX, UK
Reinforcement Learning with Metacognitive Feedback Elicits Faithful Uncertainty Expression in LLMs
Jun 30, 2026Gabrielle Kaili-May Liu, Avi Caciularu, Gal Yona +2RL for Language Models Reinforcement Learning
Yale University · Google Research
CoMet: Context and Multiplicity Decomposition for Multimodal Uncertainty Estimation
Jun 30, 2026Sanghyuk Chun, William Yang, Amaya Dharmasiri +1Uncertainty Quantification Multimodal Large Language Models
Princeton University
Wisdom Of The (AI) Crowd: Investigating Artificial Swarm Intelligence In Large Language Models
Jun 30, 2026Justin Brenne, Christian MeskeLLM Uncertainty Estimation Language Model Ensembles
Ruhr-University Bochum, Bochum, Germany · Ruhr-Universitaet Bochum, Bochum, Germany
Uncertainty-Aware Generation and Decision-Making Under Ambiguity
Jun 29, 2026Nico Daheim, Iryna GurevychIntelligent Tutoring Systems LLM Decision-Making
Ubiquitous Knowledge Processing Lab (UKP Lab), Department of Computer Science, Technical University of Darmstadt · National Research Center for Applied Cybersecurity ATHENE, Germany
Reported Confidence in LLMs Tracks Commitment More Than Correctness
Jun 28, 2026Dharshan KumaranConfidence Estimation in Language Models LLM Interpretability
Google DeepMind
AB-RAG: Adaptive Budgeted Retrieval-Augmented Generation for Reliable Question Answering
Jun 27, 2026Ansh KamthanRetrieval-Augmented Generation LLM Answer Verification
Department of Artificial Intelligence and Machine Learning Manipal University Jaipur Jaipur, India
From Signals to Transfer: A Factorised Study of Probe-Based Uncertainty Estimation in Large Language Models
Jun 26, 2026Ponhvoan Srey, Xiaobao Wu, Cong-Duy Nguyen +3Uncertainty Quantification Hallucination in Language Models
Nanyang Technological University · Shanghai Jiao Tong University · VinUniversity +1
Bayesian control for coding agents
Jun 23, 2026Theodore Papamarkou, Vladislav Smirnov, Viktor Mazanov +4Language Model-Based Control Coding Agents
1PolyShape · 3MBZUAI
Beyond Logprobs: A Multi-Signal Confidence Engine for LLM-Based Document Field Extraction
Jun 23, 2026Nitesh KumarSelective Prediction Document Information Extraction
1Perfios Software Solutions Pvt. Ltd.
CALIBER: Calibrating Confidence Before and After Reasoning in Language Models
Jun 23, 2026Conor Finlay, Joshua Kurien, Saurabh Dash +2Confidence Estimation in Language Models Language Model Calibration
1Cohere Labs
The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models
Jun 22, 2026Xiang-Jun Ou, Shuang Liang, Xin-Yu Hu +3LLM Evaluation Uncertainty Quantification
National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210063, China. · School of Intelligent Science and Technology, Nanjing University, Suzhou 215163, China.
The Score Granularity Gap in Black-Box LLM Classification: A Comparative Study of Confidence Constructions
Jun 20, 2026Ao Sun, Tian Sun, Jiaxing GengLLM Evaluation Selective Prediction
A Systematic Evaluation of Black-Box Uncertainty Estimation Methods for Large Language Models
Jun 18, 2026Jiayi Wang, Xu-Yao ZhangLLM Evaluation Uncertainty Quantification
School of Advanced Interdisciplinary Sciences, University of Chinese Academy of Sciences, Beijing 100049, China · State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
AURA: Adaptive Uncertainty-aware Refinement for LLM-as-a-Judge Auditing
Jun 18, 2026Zilong Zhang, Yi-Ting Hung, Weiyi He +3LLM-as-a-Judge LLM Auditing
Department of Mathematics and Statistics Georgia State University · Department of Probability and Statistics Department of Computer Science and Engineering Michigan State University · Department of Mathematics and Statistics Concordia University +1
Uncertainty Decomposition for Clarification Seeking in LLM Agents
Jun 17, 2026Gregory MatsnevLLM Agents LLM Uncertainty Estimation
AI Talent Hub, ITMO University Saint Petersburg 197101, Russia
LLM Doesn't Know What It Doesn't Know: Detecting Epistemic Blind Spots via Cross-Model Attribution Divergence on Clinical Tabular Data
Jun 17, 2026Akshat Dasula, Prasanna Desikan, Jaideep SrivastavaLanguage Model Calibration LLM Uncertainty Estimation
Centific AI Research · Department of Computer Science & Engineering, University of Minnesota-Twin Cities, Minneapolis, USA.
A Risk Decomposition Framework for Pre-Hoc Fine-Tuning Prediction
Jun 16, 2026Yuxiang Luo, Chen Wang, Nan TangFine-Tuning LLM Fine-Tuning
The Hong Kong University of Science and Technology (Guangzhou).
Quantifying Consistency in LLM Logical Reasoning via Structural Uncertainty
Jun 15, 2026Baishali Chaudhury, Mengdie Flora Wang, Hyunji Hayley Park +3Reasoning Consistency in Language Models LLM Reliability
AWS Generative AI Innovation Center

Jul 9, 2026, cs.AIJ/K move · Enter open · S save

When LLMs Agree, Are They Right? Auditing Self-Consistency and Cross-Model Agreement as Confidence Signals

Kaihua Ding

University of Pennsylvania

LLM-as-a-Judge LLM Reliability LLM Uncertainty Estimation Self-Consistency in Language Models

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LLM-as-judge (Zheng et al., 2023) is increasingly the default for evaluating AI systems in enterprise pipelines, often scaled to ensembles (Verga et al., 2024) or "mixture-of-experts" (Shazeer et al., 2017) panels of judges. These systems share a key assumption: that consistency -- agreement among judges, or among a model's own samples -- indicates correctness. We show this assumption is unreliable. Agreement is not accuracy: a model can agree with itself, and different models can agree with each other, out of shared bias, a memorized heuristic, or an option-position prior rather than truth. We ask when agreement is nonetheless a usable proxy, in a large-scale cross-runner study: 53 runners drew K=50 samples for assigned overlapping cases across comparisons of model tier, prompting, and scale on GPQA Diamond and AIME -- 265,000 samples. Using majority-correctness as the deployment label and a hierarchical runner-clustered bootstrap, agreement is a positive but weak predictor (rho 0.20-0.59, all positive under item-clustered resampling) whose usefulness is regime-dependent: best for unsaturated mid-tier models and for allocating compute, and worst -- over-confident yet no more accurate -- for the most consistent frontier model (agreement >=0.8 on 77% of GPQA case-result entries, 48% of those wrong). An exploratory cross-family check on three Claude tiers shows the same frontier over-confidence, with confident errors recurring across providers above a marginal-preserving null. Self-consistency is thus a conditional proxy for correctness, not a standalone confidence score. We publicly release the de-identified per-run rows and answer distributions.

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