Model Misspecification

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Overview

The field studies the consequences of using models that omit variables, impose incorrect structure, or fail to capture changes across environments. Approaches include robust estimation such as median-of-means methods, posterior predictive calibration, residual and identifiability diagnostics, and discrepancy correction; applications span forecasting, physics-informed learning, simulation-based inference, and reinforcement learning. A central challenge is distinguishing genuine misspecification from noise, weak data, or poor optimization, then correcting it without erasing useful uncertainty or real effects. Because no diagnostic can establish that a model is fully correct, reliable practice often combines complementary checks with iterative model revision.

Latest papers

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29 papers8 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

  1. Contracting with an Agent under Model Mismatch

    Oct 16, 2026Elif Yılmaz, Jakub Tłuczek, Victor Villin +1Principal-Agent TheoryModel Misspecification

    University of Neuchâtel Neuchâtel, Switzerland

  2. Sign Betting for Misspecified Regression: Variance Adaptivity and Optimal Approximation

    Oct 14, 2026Yinan Li, Han Han, Kwang-Sung JunStatistical Learning TheoryModel Misspecification

    Department of Computer Science, University of Arizona · GSAI/CSE, POSTECH

  3. On the Optimality of Random Features under General Conditions: Misspecified Cases and Robust Learning

    Oct 13, 2026Caixing WangRandom Feature MethodsKernel Ridge Regression

    School of Statistics and Data Science, Southeast University, Nanjing, China

  4. Why Linear Models Can Still Work in a Complex and Changing World

    Oct 9, 2026Fangzhou Lin, Dengzhe Hou, Yinxing Li +1Model SelectionLinear Regression

    Texas A&M University, USA · Tohoku University, Japan

  5. Predictively Oriented Gaussian Process Posteriors

    Oct 2, 2026Callum Lau, Jeremias Knoblauch, Louis SharrockGaussian Process RegressionUncertainty Quantification

    University College London

  6. Beyond Model Ranking: Regime Diagnosis for Distributional-Statistical Misspecification in Industrial Time-Series Forecasting

    Sep 30, 2026Pengyu Nie, Chenglang Xu, Yaoshi Chen +4Time Series ForecastingForecasting Benchmarks

    JD.com, Inc.

  7. Who Verifies the Graph? Misspecification Attacks on Causal Action Verification for Language Agents

    Sep 30, 2026Fabio RovaiAI Agent AuditingModel Misspecification

    The Tesseract Academy London, United Kingdom

  8. Scalable Diffusion SBI for Compositional Inference under Simulator Misspecification

    Sep 29, 2026Vincent D. Zaballa, Elliot E. HuiSimulation-Based InferenceDiffusion Model Fine-Tuning

    University of California, Irvine

  9. Towards Identifiable Representations under Misspecified Structure

    Sep 27, 2026Yuke Li, Yujia Zheng, Ziyi Chen +2Latent Variable ModelsRepresentation Identifiability

    University of Maryland College Park, College Park, MD, USA · Carnegie Mellon University, Pittsburgh PA, USA

  10. Knowledge Distillation under Teacher Misspecification: An Order-Parameter Analysis of the Gap between Teacher Mimicry and Task Performance

    Aug 29, 2026Kazuyuki Hara, Hideitsu HinoKnowledge DistillationModel Misspecification

    Nihon University · The Institute of Statistical Mathematics

  11. An Enclosed Mode Is a Gauge Choice: Topology Relative to Reach in Certified Code World Models

    Aug 28, 2026Javier Aguilar MartínCode World ModelsWorld Models

    AGILabs

  12. Learning to Detect Symbolic Failure: Machine Learning and the Limits of Black-Scholes

    Aug 24, 2026Juli Huang, Jake Cheng, Rupert LuQuantitative FinanceRepresentation Learning

    Stanford University

  13. Unknown Unknowns: Model Misspecification in Machine Learning for Physics

    Aug 13, 2026Juan Cruz-Martinez, Carolina Cuesta-Lazaro, Alexander Held +1Model MisspecificationScientific ML

    Departamento de Física Atómica, Molecular y Nuclear, Universidad de Sevilla, E-41080 Sevilla, Spain · Center for Computational Astrophysics, Flatiron Institute, 162 Fifth Avenue, New York, NY 10010, USA · Institute for Advanced Study, 1 Einstein Drive, Princeton, NJ 08540, USA +2

  14. High-dimensional networks and mean squared error for possibly misspecified models

    Aug 13, 2026Lourens WaldorpModel SelectionMinimum Description Length

    University of Amsterdam, Nieuwe Achtergracht 129-B, 1018 NP, the Netherlands

  15. Wrong Operator or Blind Design? A Post-Training Diagnostic for Physics-Informed Coefficient Learning

    Aug 4, 2026Eric FockParameter EstimationParameter Identifiability

    PIMENT Laboratory, Université de La Réunion, Le Tampon 97430, La Réunion, France

  16. Calibration, Not Compilation: Detecting and Repairing Misspecified Probabilistic Programs Written by Language Models

    Jun 30, 2026Jian Xu, Delu Zeng, John Paisley +1Automated Program RepairBayesian Inference

    RIKEN iTHEMS · RIKEN AIP · South China University of Technology +1

  17. Silent Failures in Physics-Informed Neural Networks: Parameter Poisoning and the Limits of Loss-Based Validation

    Jun 23, 2026David McShannon, Nicholas DietrichModel MisspecificationPhysics-Informed ML

  18. Where Is My Physics Wrong? Localized and Identifiable Discovery of Model Discrepancy

    Jun 22, 2026Yifan WangSparse Identification of Nonlinear DynamicsModel Misspecification

    Department of Mechanical Engineering, McGill University, QC, H3A 2T7, Canada

  19. MA-SBI: Misspecification-Aware Simulation-Based Inference via Side-Channel Guidance

    Jun 15, 2026Arunkumar V, Manoranjan Gandhudi, Gangadharan G. R. +2Simulation-Based InferenceParameter Estimation

    University College of Engineering, Anna University Tiruchirappalli, Tamil Nadu, India · Central University of Karnataka, India · National Institute of Technology Tiruchirappalli, India +1

  20. Online KL-Regularized Reinforcement Learning with Function Approximation under Misspecification

    Jun 4, 2026Haoyang Hong, Zichen Wang, Quanquan Gu +1Regret Minimization in RLReinforcement Learning

    School of Electrical Engineering and Computer Science, Oregon State University · Department of Electrical and Computer Engineering and Coordinated Science Laboratory, University of Illinois Urbana-Champaign · Department of Computer Science, University of California, Los Angeles

  21. When Should an AI Scientist Stop? Verifiable Experiment Steering and Refusal for Autonomous Discovery

    May 26, 2026Neel Tushar Shah, Manglam KartikModel MisspecificationAutonomous Scientific Discovery

    Indian Institute of Technology Bombay, Mumbai, India.

  22. Infra-Bayesian Reinforcement Learning Agents Outperform Classical RL For Worst-Case Robustness

    May 22, 2026Manish Aryal, Faiyaz Azam, Agnivo Banerjee +11Reinforcement LearningModel Misspecification

    Purdue University · Carnegie Mellon University · WorldQuant University +11

  23. Robust Sequential Experimental Design for A/B Testing

    May 13, 2026Qianglin Wen, Xiangkun Wu, Chengchun Shi +4Causal Effect EstimationModel Misspecification

    Yunnan Key Laboratory of Statistical Modeling and Data Analysis, Yunnan University, Kunming, China · School of Mathematical Sciences, Zhejiang University, Hangzhou, China · Department of Statistics, London School of Economics and Political Science, London, United Kingdom +3

  24. Information-Preserving Domain Transfer with Unlabeled Data in Misspecified Simulation-Based Inference

    May 7, 2026Joon Jang, Eunho Jeong, Kyu Sung Choi +1Synthetic-to-Real Domain AdaptationSimulation-Based Inference

    Department of Biomedical Sciences, Seoul National University, Seoul, Republic of Korea · Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Republic of Korea · Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea +3

  25. Dynamic Decision-Making under Model Misspecification: A Stochastic Stability Approach

    Feb 19, 2026Xinyu Dai, Daniel Chen, Yian QianThompson SamplingModel Misspecification

    Department of Economics, Brown University. · Department of Applied Mathematics, Brown University.

  26. Omitted Variable Bias in Language Models Under Distribution Shift

    Feb 18, 2026Victoria Lin, Louis-Philippe Morency, Eli Ben-MichaelOOD GeneralizationGeneralization Bounds

    Carnegie Mellon University, Pittsburgh, PA, USA

  27. Dissecting the Impact of Model Misspecification in Data-driven Optimization

    Mar 1, 2025Adam N. Elmachtoub, Henry Lam, Haixiang Lan +1Model MisspecificationDecision-Focused Learning

    Columbia University · Morgan Stanley

  28. Predictive variational inference: Learn the predictively optimal posterior distribution

    Oct 18, 2024Jinlin Lai, Antonio Linero, Yuling YaoModel MisspecificationVariational Inference

    College of Information and Computer Sciences, University of Massachusetts Amherst · Department of Statistics and Data Sciences, University of Texas, Austin

  29. Robust Linear Predictions: Analyses of Uniform Concentration, Fast Rates and Model Misspecification

    Jan 6, 2022Saptarshi Chakraborty, Debolina Paul, Swagatam DasRobust RegressionModel Misspecification

    Department of Statistics, University of California, Berkeley · Department of Statistics, Stanford University · Electronics and Communication Sciences Unit, Indian Statistical Institute, Kolkata, India