Selective Prediction

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32 papers in the last four weeks, up 88% on the four weeks before. 0.3% of all new papers.

Jul 13Week of Sep 28

Latest papers 176

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NoisyCoconut: Counterfactual Consensus via Latent Space Reasoning
May 6, 2026Michael Jerge, David EvansSelective Prediction LLM Inference
University of Virginia
Ask Before You Diagnose: Safe-Psych, a Sequential Evaluation Benchmark for LLMs in Psychiatry
May 6, 2026Oriana Presacan, Andreea Grama, Larisa Irimină +6Selective Prediction Healthcare
National University of Science and Technology Politehnica Bucharest · Psychiatric Hospital Doctor Gheorghe Preda · Oslo Metropolitan University +3
LLMs Uncertainty Quantification via Adaptive Conformal Semantic Entropy
May 5, 2026Hamed Karimi, Vaishali Meyappan, Reza SamaviSelective Prediction Semantic Entropy
Toronto Metropolitan University, Toronto, Ontario, Canada · Vector Institute, Toronto, Ontario, Canada
Selective Prediction from Agreement: A Lipschitz-Consistent Version Space Approach
May 4, 2026Mohamadsadegh KhosravaniSelective Prediction Active Learning
University of Regina
CHASE: Competing Hypotheses for Ambiguity-Aware Selective Prediction
May 2, 2026Kartik Jhawar, Yuhao Geng, Atul N. Parikh +1Selective Prediction Selective Inference
Institute for Digital Molecular Analytics and Science, NTU, Singapore 636921 · School of Electrical and Electronic Engineering, NTU, Singapore 639798 · School of Materials Science and Engineering, NTU, Singapore 639798 +1
Knowing when to trust machine-learned interatomic potentials
May 1, 2026Shams Mehdi, Ilkwon Cho, Olexandr IsayevMachine Learning Interatomic Potentials Selective Prediction
Department of Chemistry, Mellon College of Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, 15213, USA. · Department of Materials Science and Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania, 15213, USA. · Ray and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, 15213, USA.
Confidence Estimation in Automatic Short Answer Grading with LLMs
Apr 30, 2026Longwei Cong, Sonja Hahn, Sebastian Gombert +3Uncertainty Quantification Selective Prediction
DIPF | Leibniz Institute for Research and Information in Education, 60323 Frankfurt am Main, Germany · Faculty of Computer Science, Goethe University Frankfurt, 60629 Frankfurt am Main, Germany · Chemnitz University of Technology, 09111 Chemnitz, Germany
Geometry-Calibrated Conformal Abstention for Language Models
Apr 30, 2026Rui Xu, Yi Chen, Sihong Xie +1Uncertainty Quantification Selective Prediction
Information Hub, AI Thrust Hong Kong University of Science and Technology (Guangzhou) Guangzhou, Guangdong, China
Learning When to Remember: Risk-Sensitive Contextual Bandits for Abstention-Aware Memory Retrieval in LLM-Based Coding Agents
Apr 30, 2026Mehmet IscanSelective Prediction AI Coding Agents
PythaLab, Yildiz Technical University, Istanbul, Türkiye.
SIEVES: Selective Prediction Generalizes through Visual Evidence Scoring
Apr 28, 2026Hector G. Rodriguez, Marcus RohrbachVisual Question Answering VLM Robustness
TU Darmstadt & hessian.AI, Germany
RAS: a Reliability Oriented Metric for Automatic Speech Recognition
Apr 27, 2026Wenbin Huang, Yuhang Qiu, Bohan Li +5ASR Evaluation Selective Prediction
X-LANCE Lab, School of Computer Science, Shanghai Jiao Tong University, China · MoE Key Lab of Artificial Intelligence; Jiangsu Key Lab of Language Computing, China
Learning Selective LLM Autonomy from Copilot Feedback in Enterprise Customer Support Workflows
Apr 26, 2026Nikita Borovkov, Elisei Rykov, Olga Tsymboi +4Selective Prediction Customer Support Automation
T-Tech
MoBayes: A Modular Bayesian Framework for Separating Reasoning from Language in Conversational Clinical Decision Support
Apr 21, 2026Yusuf Kesmen, Fay Elhassan, Jiayi Ma +7Selective Prediction Clinical Decision Support
LiGHT, EPFL · University of Bern · CLAN, Aarhus University
Learning When Not to Decide: A Framework for Overcoming Factual Presumptuousness in AI Adjudication
Apr 21, 2026Mohamed Afane, Emily Robitschek, Derek Ouyang +1AI-Assisted Decision Making Structured Prompting
Stanford University · Stanford, CA, USA
Concurrent Criterion Validation of a Validity Screen for LLM Confidence Signals via Selective Prediction
Apr 20, 2026Jon-Paul CacioliLLM Evaluation Selective Prediction
Independent Researcher, Melbourne, Australia
Beyond "I Don't Know": Evaluating LLM Self-Awareness in Discriminating Data and Model Uncertainty
Apr 19, 2026Jingyi Ren, Ante Wang, Yunghwei Lai +5LLM Evaluation Selective Prediction
Dept. of Comp. Sci. & Tech., Institute for AI, Tsinghua University, Beijing, China · Institute for AI Industry Research (AIR), Tsinghua University, Beijing, China
Instinct vs. Reflection: Unifying Token and Verbalized Confidence in Multimodal Large Models
Apr 19, 2026Yunkai Dang, Yifan Jiang, Yizhu Jiang +3Selective Prediction Confidence Estimation in Language Models
School of Artificial Intelligence, Nanjing University.
Complementing Self-Consistency with Cross-Model Disagreement for Uncertainty Quantification
Apr 18, 2026Kimia Hamidieh, Veronika Thost, Walter Gerych +2Selective Prediction Confidence Estimation in Language Models
1MIT · MIT-IBM Watson AI Lab · 3WPI +1
SegWithU: Uncertainty as Perturbation Energy for Single-Forward-Pass Risk-Aware Medical Image Segmentation
Apr 16, 2026Tianhao Fu, Austin Wang, Charles Chen +2Image Segmentation Uncertainty Quantification
University of Toronto, Toronto, ON, Canada · Vector Institute, Toronto, ON, Canada · Project Neura, Toronto, ON, Canada +4
Data-Driven Priors for Uncertainty-Aware Risk Prediction of Clinical Deterioration using Multimodal Data
Mar 9, 2026L. Julián Lechuga López, Tim G. J. Rudner, Farah E. ShamoutBayesian Neural Networks Uncertainty Quantification
Tandon School of Engineering, New York University · New York University Abu Dhabi · University of Toronto
No Certificate, No Categorical Speech Act: A Brouwerian Assertibility Constraint for Public Reason
Mar 4, 2026Michael JülichAI Accountability Selective Prediction
aihorizon R&D, Munich, Germany.
When Agents Disagree With Themselves: Behavioral Consistency as an Uncertainty Signal for LLM Agents
Feb 12, 2026Aman MehtaAI Agent Reliability Selective Prediction
Snowflake AI Research
MedBayes-Lite: A Clinical Uncertainty Governance Layer for Risk-Aware Medical Decision Support
Nov 20, 2025Elias Hossain, Md Mehedi Hasan Nipu, Maleeha Sheikh +5Selective Prediction Healthcare
College of Engineering and Computer Science, University of Central Florida, Orlando, FL 32816, USA · Department of Computer Science and Engineering, North South University, Dhaka 1229, Bangladesh · Department of Electrical and Computer Engineering, Purdue University Fort Wayne, Fort Wayne, IN 46805, USA +4
Unequal Uncertainty: Rethinking Algorithmic Interventions for Mitigating Discrimination from AI
Aug 11, 2025Holli Sargeant, Mackenzie Jorgensen, Arina Shah +3Selective Prediction Algorithmic Fairness
University of Cambridge, United Kingdom · Northumbria University, Newcastle, United Kingdom · George Washington University, Washington D.C., United States +2
Verify when Uncertain: Beyond Self-Consistency in Black Box Hallucination Detection
Feb 20, 2025Yihao Xue, Kristjan Greenewald, Youssef Mroueh +1Kernel Mean Embeddings Selective Prediction
Department of Computer Science University of California, Los Angeles · MIT-IBM Watson AI Lab · IBM Research
From Few-Shot Segmentation to Clinician-in-the-Loop Medical Image Analysis
Date pendingYazhou ZhuInteractive Medical Image Segmentation Selective Prediction

May 6, 2026, cs.LGJ/K move · Enter open · S save

NoisyCoconut: Counterfactual Consensus via Latent Space Reasoning

Michael Jerge, David Evans

University of Virginia

Selective Prediction LLM Inference LLM Reliability LLM Uncertainty Estimation

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This paper presents NoisyCoconut, a novel inference-time method that enhances large language model (LLM) reliability by manipulating internal representations. Unlike fine-tuning methods that require extensive retraining, NoisyCoconut operates directly on model representations during inference and requires no retraining. Rather than training models to reason in latent space, we inject controlled noise into latent trajectories to generate diverse reasoning paths. Agreement among these paths provides a confidence signal, enabling models to abstain when uncertain. We demonstrate that this approach achieves effective coverage-accuracy tradeoffs across multiple reasoning benchmarks without requiring access to training data or modification of model parameters. This approach provides a practical pathway to improving the reliability of LLM outputs while maintaining compatibility with existing models. Our experiments show that unanimous agreement among noise-perturbed paths reduces error rates from 40-70% to below 15%, enabling models to exceed 95% accuracy on mathematical reasoning tasks through selective abstention.

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