Autoregressive Modeling

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7 papers in the last four weeks, up 40% on the four weeks before. 0.1% of all new papers.

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Comparative Study of Neural Surrogate Architectures for Autoregressive Prediction of Internal Battery States
Jun 18, 2026Gihyun Lee, Thorben Menne, Simon Olma +2Neural Surrogate Modeling PDE Surrogate Modeling
Technische Universität Berlin, Berlin, Germany · IAV GmbH, Berlin, Germany
DTVEM-RE: A Hierarchical Random-Effects Extension of the Differential Time-Varying Effect Model for Person-Specific Multi-Lag Estimation in Intensive Longitudinal Data
Jun 12, 2026Amartya BhattacharyaIrregular Time-Series Modeling Hierarchical Bayesian Modeling
Geisel School of Medicine, Dartmouth College
Two-Layer Linear Auto-Regressive Models Estimate Latent States
Jun 10, 2026Yahya Sattar, Sunmook Choi, Leo Maynard-Zhang +3Dynamical Systems Kalman Filtering
Cornell · U Washington · Imperial College London +1
When Do Autoregressive Sequence Models Forecast Physical Wavefields? A Controlled Study on Synthetic Seismograms
Jun 9, 2026Waleed Esmail, Stuart Russell, Jana Klinge +2Multi-Token Prediction Seismology
Institut für Kernphysik, Universität Münster Wilhelm-Klemm-Straße 9, 48149 Münster, Germany · Institut für Geophysik, Universität Münster Corrensstraße 24, 48149 Münster, Germany · James Cook University, 1 James Cook Drive, Douglas, QLD 4814, Australia +1
Divide-and-Conquer Modeling for the CTF-4-Science Lorenz Benchmark
Jun 8, 2026Shundong LiForecasting Benchmarks Chaotic Dynamical Systems
Worcester Polytechnic Institute Worcester, MA, USA
Data-Driven Forecasting of three-Component Seismograms Using Transformer Architectures
Jun 1, 2026Waleed Esmail, Stuart Russell, Jana Klinge +2Multivariate Time Series Forecasting Time Series Forecasting
Institut für Kernphysik, Universität Münster, Wilhelm-Klemm-Straße 9, 48149, Münster · Institut für Geophysik, Universität Münster, Corrensstraße 24, 48149, Münster · James Cook University, 1 James Cook Drive, Douglas, Queensland, 4814, Australia +1
(HB-ARFM) History-Bootstrapped Flow Matching for Inverse Boiling Reconstruction
May 29, 2026Xianwei Zou, Sheikh Md Shakeel Hassan, Arthur Feeney +1Flow Matching PDE Inverse Problems
Department of Electrical Engineering and Computer Science, University of California Irvine, USA.
Flow map learning in nonlinear vector autoregressive models: influence of the feature-library structure on the training error
May 29, 2026Markus GrossKoopman Operator Learning Nonlinear System Identification
Institute for AI Safety and Security, German Aerospace Center (DLR), Sankt Augustin and Ulm, Germany
Compositional Generalization in Autoregressive Models via Logit Composition
May 27, 2026Aakash Kumar, Maria Sofia Bucarelli, Emanuele NataleLanguage Model Merging Compositional Generalization
COATI, CNRS, Inria, I3S, Université Coté d’Azur, France
Sequential Neural Probabilistic Amplitude Shaping: Learning the Channel's Language
May 27, 2026Mohammad Taha Askari, Lutz Lampe, Amirhossein GhazisaeidiDistribution Matching Autoregressive Modeling
Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, Canada · Nokia Bell Labs, 12 rue Jean Bart, 91300 Massy, France
Neural Autoregressive Control Variates for the Quantum Monte Carlo Sign Problem
May 26, 2026Bei Qiao, Lei WangQuantum Many-Body Physics Variance Reduction
Beijing National Laboratory for Condensed Matter Physics and Institute of Physics, Chinese Academy of Sciences, Beijing 100190, China · University of Chinese Academy of Sciences, Beijing 100049, China
Causal Discovery in Structural VAR Models Under Equal Noise Variance
May 21, 2026SeyedSina Seyedi HasanAbadi, Fahimeh Arab, Erfan Nozari +1Time-Series Causal Discovery Causal Discovery
Bourns College of Engineering, University of California, Riverside · University of California, San Francisco · Department of Mathematics and Statistics, Boston University
Reviving Error Correction in Modern Deep Time-Series Forecasting
May 20, 2026Minh Hoang Nguyen, Dai Do, Huu Hiep Nguyen +3Long-Term Time Series Forecasting Time Series Forecasting
Deakin Applied AI Initiative, Deakin University, Australia.
Token by Token, Compromised: Backdoor Vulnerabilities in Unified Autoregressive Models
May 19, 2026Tobias Braun, Jonas Henry Grebe, Hossein Shakibania +2Unified Multimodal Models Backdoor Attacks
TU Darmstadt & hessian.AI, Germany
McCast: Memory-Guided Latent Drift Correction for Long-Horizon Precipitation Nowcasting
May 13, 2026Penghui Wen, Yu Luo, Lintao Wang +4Memory-Augmented Neural Networks Precipitation Forecasting
School of Computer Science, The University of Sydney, Australia · School of Life and Environmental Science, The University of Sydney, Australia
Autoregressive Learning in Joint KL: Sharp Oracle Bounds and Lower Bounds
May 12, 2026Yunbei Xu, Yuzhe Yuan, Ruohan ZhanStatistical Learning Theory Information-Theoretic Lower Bounds
National University of Singapore · University College London
Controlling Transient Amplification Improves Long-horizon Rollouts
May 9, 2026Adeel Pervez, Francesco LocatelloJacobian Regularization Spatiotemporal Forecasting
Institute of Science and Technology Austria Klosterneuburg, Austria
The Power of Second Order Methods for Sequence Preconditioning
May 8, 2026Annie Marsden, Elad HazanDynamical Systems Sequence Modeling
Google DeepMind · Princeton University
Order-Agnostic Autoregressive Modelling with Missing Data
May 7, 2026Ignacio Peis, Pablo M. Olmos, Jes FrellsenIncomplete Data Imputation Learning with Missing Data
Technical University of Denmark & Pioneer Centre for AI · Universidad Carlos III de Madrid
Towards Scalable One-Step Generative Modeling for Autoregressive Dynamical System Forecasting
May 7, 2026Tianyue Yang, Xiao XuePDE Surrogate Modeling One-Step Generative Modeling
The Center for Computational Science University College London
Rethinking Entropy Minimization in Test-Time Adaptation for Autoregressive Models
May 5, 2026Wei-Ping Huang, Chee-En Yu, Guan-Ting Lin +1Test-Time Adaptation Entropy Regularization
Graduate Institute of Communication Engineering, National Taiwan University, Taipei, Taiwan
Barriers to Counterfactual Credit Attribution for Autoregressive Models
May 2, 2026Aloni Cohen, Chenhao ZhangGenerative Modeling Counterfactual Credit Assignment
Sampling two-dimensional spin systems with transformers
Apr 30, 2026Piotr Białas, Piotr Korcyl, Tomasz Stebel +2Transformer Ising Model
Institute of Applied Computer Science, Jagiellonian University, ul. Łojasiewicza 11, 30-348 Kraków, Poland · Institute of Theoretical Physics, Jagiellonian University, ul. Łojasiewicza 11, 30-348 Kraków, Poland · Doctoral School of Exact and Natural Sciences, Jagiellonian University, ul. Łojasiewicza 11, 30-348 Kraków, Poland
ABC: Any-Subset Autoregression via Non-Markovian Diffusion Bridges in Continuous Time and Space
Apr 30, 2026Gabe Guo, Thanawat Sornwanee, Lutong Hao +3Autoregressive Diffusion Generative Modeling
Stanford University
A Hybridizable Neural Time Integrator for Stable Autoregressive Forecasting
Apr 22, 2026Brooks Kinch, Xiaozhe Hu, Yilong Huang +6Neural Surrogate Modeling Time Series Transformers
Mechanical Engineering and Applied Mechanics, University of Pennsylvania, Philadelphia, PA, USA · Department of Mathematics, Tufts University, Medford, MA, USA · Department of Mathematics and Cybernetics, SINTEF Digital, Oslo, Norway +3
Revisiting Autoregressive Models for Generative Image Classification
Mar 19, 2026Ilia Sudakov, Artem Babenko, Dmitry BaranchukVisual Autoregressive Models Image Classification
1HSE University · 2Yandex Research
NOVA: Next-step Open-Vocabulary Autoregression for 3D Multi-Object Tracking in Autonomous Driving
Mar 6, 2026Kai Luo, Xu Wang, Rui Fan +1Visual Object Tracking Autonomous Driving
School of Artificial Intelligence and Robotics and the National Engineering Research Center of Robot Visual Perception and Control Technology, Hunan University, Changsha 410082, China · College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China · State Key Laboratory of Intelligent Autonomous Systems, Tongji University, Shanghai 201804, China
Spatial Autoregressive Modeling of DINOv3 Embeddings for Unsupervised Anomaly Detection
Mar 3, 2026Ertunc Erdil, Nico Schulthess, Guney Tombak +1Medical Image Anomaly Detection Industrial Anomaly Detection
Computer Vision Lab. ETH Zurich, Zurich, Switzerland
ArchesClimate: Probabilistic Decadal Ensemble Generation With Flow Matching
Sep 19, 2025Graham Clyne, Guillaume Couairon, Guillaume Gastineau +2Neural Surrogate Modeling Climate Science
ARCHES, INRIA, Paris, France · UMR LOCEAN, IPSL, Sorbonne Universit´e, IRD, CNRS, MNHN, Paris, France · Department of Computer Science, University of Colorado Boulder, USA
Temporal horizons in forecasting: a performance-learnability trade-off
Jun 4, 2025Pau Vilimelis Aceituno, Jack William Miller, Noah Marti +2Long-Term Time Series Forecasting Time Series Forecasting
Institute of Neuroinformatics, ETH Zürich and University of Zürich, Winterhurerstrasse 190, Zürich 8057, Switzerland · School of Computing, National Australian University, 108 North Rd, Acton ACT 2601, Australia · Unit of Land Change Science, Swiss Federal Research Institute for Forest, Snow and Landscape Zürcherstrasse 111, Birmensdorf 8903, Switzerland

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Comparative Study of Neural Surrogate Architectures for Autoregressive Prediction of Internal Battery States

Gihyun Lee, Thorben Menne, Simon Olma, Jakob Hilgert+1

Technische Universität Berlin, Berlin, Germany · IAV GmbH, Berlin, Germany

Neural Surrogate Modeling PDE Surrogate Modeling Lithium-Ion Batteries Neural Operators Autoregressive Modeling

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The Doyle-Fuller-Newman (DFN) model resolves internal electrochemical states in lithium-ion batteries with high fidelity. However, the numerical solution of its governing equations is computationally prohibitive for real-time deployment, limiting scalability from individual cells to pack and fleet-scale applications. While machine learning surrogates can substantially reduce inference latency through GPU acceleration, most existing approaches learn solution approximations tied to specific operating conditions rather than learning generalizable state-evolution dynamics. This work presents a systematic comparison of four neural network architectures (MLP, ResNet, U-Net, FNO) formulated as autoregressive state-transition operators that predict full DFN internal states across a wide range of operating conditions. To ensure a controlled architectural comparison, all models are trained under a unified framework using multi-step unrolling and current-conditioning, isolating the impact of spatial inductive bias. Results demonstrate that the U-Net's multi-scale feature hierarchy achieves a mean final-step nRMSE of 3% averaged across all internal state variables after 300-step autoregressive rollouts, while providing a 5.38x speed-up over the numerical solver. These findings highlight spatial inductive bias as a critical determinant of surrogate performance, advancing the development of surrogates for internal state observability for next-generation battery management systems and digital twins.

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