Model Misspecification

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

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Beyond Model Ranking: Regime Diagnosis for Distributional-Statistical Misspecification in Industrial Time-Series Forecasting
Sep 30, 2026Pengyu Nie, Chenglang Xu, Yaoshi Chen +4Time Series Forecasting Forecasting Benchmarks
JD.com, Inc.
Who Verifies the Graph? Misspecification Attacks on Causal Action Verification for Language Agents
Sep 30, 2026Fabio RovaiAI Agent Auditing Model Misspecification
The Tesseract Academy London, United Kingdom
Towards Identifiable Representations under Misspecified Structure
Sep 27, 2026Yuke Li, Yujia Zheng, Ziyi Chen +2Latent Variable Models Representation Identifiability
University of Maryland College Park, College Park, MD, USA · Carnegie Mellon University, Pittsburgh PA, USA
High-dimensional networks and mean squared error for possibly misspecified models
Aug 13, 2026Lourens WaldorpModel Selection Minimum Description Length
University of Amsterdam, Nieuwe Achtergracht 129-B, 1018 NP, the Netherlands
Wrong Operator or Blind Design? A Reference-Free Diagnostic for Physics-Informed Coefficient Learning
Aug 4, 2026Eric FockParameter Estimation Parameter Identifiability
PIMENT Laboratory, Universit´e de La R´eunion, Le Tampon 97430, La R´eunion, France
Calibration, Not Compilation: Detecting and Repairing Misspecified Probabilistic Programs Written by Language Models
Jun 30, 2026Jian Xu, Delu Zeng, John Paisley +1Automated Program Repair Bayesian Inference
RIKEN iTHEMS · RIKEN AIP · South China University of Technology +1
Silent Failures in Physics-Informed Neural Networks: Parameter Poisoning and the Limits of Loss-Based Validation
Jun 23, 2026David McShannon, Nicholas DietrichModel Misspecification Physics-Informed ML
Where Is My Physics Wrong? Localized and Identifiable Discovery of Model Discrepancy
Jun 22, 2026Yifan WangSparse Identification of Nonlinear Dynamics Model Misspecification
Department of Mechanical Engineering, McGill University, QC, H3A 2T7, Canada
MA-SBI: Misspecification-Aware Simulation-Based Inference via Side-Channel Guidance
Jun 15, 2026Arunkumar V, Manoranjan Gandhudi, Gangadharan G. R. +2Simulation-Based Inference Parameter Estimation
University College of Engineering, Anna University Tiruchirappalli, Tamil Nadu, India · Central University of Karnataka, India · National Institute of Technology Tiruchirappalli, India +1
Online KL-Regularized Reinforcement Learning with Function Approximation under Misspecification
Jun 4, 2026Haoyang Hong, Zichen Wang, Quanquan Gu +1Regret Minimization in RL Reinforcement 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
When Should an AI Scientist Stop? Verifiable Experiment Steering and Refusal for Autonomous Discovery
May 26, 2026Neel Tushar Shah, Manglam KartikModel Misspecification Autonomous Scientific Discovery
Indian Institute of Technology Bombay, Mumbai, India.
Infra-Bayesian Reinforcement Learning Agents Outperform Classical RL For Worst-Case Robustness
May 22, 2026Manish Aryal, Faiyaz Azam, Agnivo Banerjee +11Reinforcement Learning Model Misspecification
Purdue University · Carnegie Mellon University · WorldQuant University +11
Robust Sequential Experimental Design for A/B Testing
May 13, 2026Qianglin Wen, Xiangkun Wu, Chengchun Shi +4Causal Effect Estimation Model 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
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 Adaptation Simulation-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
Predictive variational inference: Learn the predictively optimal posterior distribution
Oct 18, 2024Jinlin Lai, Antonio Linero, Yuling YaoModel Misspecification Variational Inference
College of Information and Computer Sciences, University of Massachusetts Amherst · Department of Statistics and Data Sciences, University of Texas, Austin
Robust Linear Predictions: Analyses of Uniform Concentration, Fast Rates and Model Misspecification
Jan 6, 2022Saptarshi Chakraborty, Debolina Paul, Swagatam DasRobust Regression Model Misspecification
Department of Statistics, University of California, Berkeley · Department of Statistics, Stanford University · Electronics and Communication Sciences Unit, Indian Statistical Institute, Kolkata, India

Sep 30, 2026, cs.LGJ/K move · Enter open · S save

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

Pengyu Nie, Chenglang Xu, Yaoshi Chen, Chaogan Ren+3

JD.com, Inc.

Time Series Forecasting Forecasting Benchmarks Model Misspecification

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Time-series forecasting models achieve strong benchmark performance but exhibit severe systematic bias in industrial deployments. This train--deploy gap is conventionally attributed to temporal-structural errors or distribution shifts. We characterize a complementary source that these explanations overlook: canonical losses embed fixed statistical priors, while industrial demand mixes benign and pathological regimes---zero-inflation, skewness, high variability---in which these priors are systematically violated. The induced bias persists even under perfect temporal modeling, remains in a distributional-shape component that normalization cannot remove, and creates an aggregation trade-off invisible to aggregate metrics. We turn these observations into an evaluation toolkit centered on the Regime-wise Relative Bias Vector (RBV): a metric-agnostic, regime-decomposed diagnostic that audits how pooled training allocates systematic mismatch across pathological subpopulations. A controlled attribution analysis decomposes RBV into a model-independent intrinsic floor, set by each loss's estimand, and an excess component attributable to training, tracing observed bias to the loss rather than the model. A large-scale study---13 loss objectives, 3 seeds, 60,000+ series spanning RetailShiftBench and M5, with random-split controls---shows that regime-aware diagnosis separates optimization-type from bias-type failure, and that regime-aware training resolves the pooling-induced bias that capacity scaling cannot, for mean-type losses. A formal structural observation, that risk under evaluation-distribution contamination is affine in the pathology mixture weight, grounds these findings. Our work complements model ranking with mechanism-grounded, regime-oriented evaluation.

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