Drifting Models

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5 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

Jul 13Week of Sep 28

Latest papers 37

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One-Step Generative Surrogate Models via Block-Triangular Joint Drifting
Sep 22, 2026Nicholas Geissler, Shreya Jha, Ricardo Baptista +1Surrogate Modeling One-Step Generative Modeling
Courant Institute of Mathematical Sciences, New York University · Statistical Sciences, University of Toronto · Institute of Mathematics, EPFL
Concept Drift from a Causal Perspective
Sep 21, 2026Eduardo V. L. Barboza, Jean Paul Barddal, Robert Sabourin +1Concept Drift Drifting Models
LIVIA, École de Technologie Supérieure · Graduate Program in Informatics (PPGIa), Pontifícia Universidade Católica do Paraná (PUCPR)
Convergence rates for generative drifting flows: fixed-scale obstructions and multihead acceleration
Sep 14, 2026Arthur Stéphanovitch, Eddie AamariOne-Step Generative Modeling Generative Modeling
Centre de recherche en économie et statistique · ENSAE, IP Paris · Département de Mathématiques et Applications +1
A Dominant Supplier Slows Recursive Drift More Than It Steers It
Sep 10, 2026Yangze Liu, Zhongyi HanDrifting Models Recursive Language Models
Shandong University
Evaluating Model Retraining under Drift: Paired Comparisons of Cumulative Subgroup Disparity
Sep 9, 2026Aaron CerossAlgorithmic Fairness Group Fairness
Birmingham Law School, University of Birmingham
Concept Drift Detection and Adaptive Retraining of Malware Classification Models
Aug 13, 2026Christofer Washington Berruz Chungata, Martin Jurecek, Katerina Potika +2Concept Drift Malware Classification
Department of Computer Science, San Jose State University · Faculty of Information Technology, Czech Technical University in Prague
Mapping and Measuring the Behavioral Evolution of Large Language Models
Aug 11, 2026Dong Qiao, Chris Ding, Jicong FanLLM Evaluation Representation Geometry in Language Models
School of Data Science, The Chinese University of Hong Kong, Shenzhen, China
Second Order Drifting Models
Aug 8, 2026Drake Brown, Yuhao Huang, Shih-Hsin Wang +1One-Step Generative Modeling Drifting Models
Department of Mathematics Scientific Computing and Imaging (SCI) Institute University of Utah
CFD-Guided Detection of Concept Drift in Multimodal Physiologic Signals
Aug 7, 2026Farouk Ganiyu Adewumi, Timothy Oladunni, Rochak Ghimire +3Concept Drift Electrocardiography
NOMADD: Numerical Optimization of Models Adapting to Data Drift
Aug 3, 2026Swapn Shah, Keith BurghardtConcept Drift Tabular ML
University of North Carolina at Charlotte
Finite-Probe Total-Variation Certificates for Finite-Basis Drifting Models
Aug 3, 2026Sam Andersson, Ricky MolénUncertainty Quantification Drifting Models
Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden · Department of Computational Science and Technology, KTH Royal Institute of Technology, Stockholm, Sweden
When Can You Correct Distribution Drift in Temporal Graph Generation? A Sharpening--Drift Tension and an Impossibility for Observation-Based Correction
Jul 27, 2026Tianpeng Li, Xuan Guo, Wenjun Wang +2Flow Matching Distribution Shift
College of New Media and Communication, Tianjin University · School of Artificial Intelligence, Tianjin University · School of Cyberspace Security,Hangzhou Dianzi University
Self-Supervised Consistency Enhanced Disentangled Learning for Neural Decoding Generalization in Brain-Machine Interface
Jul 27, 2026Jiyu Wei, Di Hong, Zhanjie Zhang +3Disentangled Representation Learning Brain-Computer Interfaces
College of Computer Science and Technology, Zhejiang University, Hangzhou, China · Nanhu Brain-Computer Interface Institute, Hangzhou, China
Steeringless Drifting: Differential-Torque Control of a Four-Wheel Independently Driven Vehicle
Jul 26, 2026Sheng Zhao, Zexin Wu, Dongyang Zhou +2Robotic Control Autonomous Driving
School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China · School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798, Singapore
DriftWorld: Fast World Modeling through Drifting
Jul 16, 2026Susie Lu, Haonan Chen, Weirui Ye +1World Models for Robotics World Model-Based Planning
Massachusetts Institute of Technology · Harvard University
Certified World Models as Sensing Clocks: Drift-Aware Deadlines for Active Perception
Jul 1, 2026Hongbo WangActive Perception Active Sensing
Distilling Drifting Transformers with Representation Autoencoders
Jun 14, 2026Jiawei Zhang, Mengfei Xia, Gen Li +1Diffusion Model Distillation Autoencoders
Tsinghua University · Ant Group · CUHK
Who Drifted: the System or the Judge? Anytime-Valid Attribution in LLM Evaluation Pipelines
Jun 13, 2026Yitao LiLLM Evaluation LLM-as-a-Judge
DriftingMol: Decoder-Coupled Drift for One-Pass Property-Conditional Molecular Generation
May 24, 2026Jiangjie Qiu, Yijun Li, Wentao Li +1One-Step Generative Modeling Molecular Generation
Beijing Key Laboratory of Artificial Intelligence for Advanced Chemical Engineering Materials
Drift-Resistant Navigation World Model with Anchored Epipolar Guidance
May 23, 2026Po-Chien Luan, Zimin Xia, Wuyang Li +2World Models for Robotics Model-Based Planning
ContextEcho: A Benchmark for Persona Drift in Long Agentic-Coding Sessions
May 22, 2026Xianzhong Ding, Yangyang Yu, Changwei Liu +1Tool-Augmented Language Model Agents AI Coding Agents
Center for Advanced AI, Accenture
Finite-Particle Convergence Rates for Conservative and Non-Conservative Drifting Models
May 21, 2026Krishnakumar BalasubramanianOne-Step Generative Modeling Drifting Models
Department of Statistics, University of California, Davis
Drifting Objectives for Refining Discrete Diffusion Language Models
May 19, 2026Daisuke Oba, Hiroki Furuta, Naoaki OkazakiDiffusion Model Fine-Tuning Diffusion Language Models
Institute of Science Tokyo · AIST · NII LLMC
Counterfactual Explanations Under Concept Drift
May 17, 2026Marcin Kostrzewa, Jerzy Stefanowski, Maciej ZiębaCounterfactual Explanations Concept Drift
Wrocław University of Science and Technology · Pozna´n University of Technology · Tooploox
Drift Flow Matching
May 17, 2026Chenrui Ma, Xi Xiao, Lin Zhao +3Flow Matching Inference-Time Scaling
University of California, Irvine · University of Virginia · University of Alabama at Birmingham +1
DriftXpress: Faster Drifting Models via Projected RKHS Fields
May 12, 2026Ali Falahati, Elliot Creager, Gautam Kamath +1Image Generation One-Step Generative Modeling
Cheriton School of Computer Science, University of Waterloo and Vector Institute. · Department of Electrical and Computer Engineering, University of Waterloo and Vector Institute. · A*STAR Centre for Frontier AI Research, Singapore.
Kernel-Gradient Drifting Models
May 11, 2026Maria Esteban-Casadevall, Jorge Carrasco-Pollo, Max Welling +3Synthetic Data Generation One-Step Generative Modeling
AMLab · University of Amsterdam · CuspAI +1
The Geometry of Forgetting: Temporal Knowledge Drift as an Independent Axis in LLM Representations
May 9, 2026Rania Elbadry, Ahmed Heakl, Fan Zhang +4LLM Interpretability LLM Reliability
Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) · INSAIT, Sofia University
Drifting Field Policy: A One-Step Generative Policy via Wasserstein Gradient Flow
May 8, 2026Juil Koo, Mingue Park, Jiwon Choi +2Robot Policy Learning Wasserstein Gradient Flows
KAIST
SymDrift: One-Shot Generative Modeling under Symmetries
May 7, 2026Samir Darouich, Vinh Tong, Lluís Pastor-Pérez +3One-Step Generative Modeling Generative Modeling
Institute of Artificial Intelligence Institute of Theoretical Chemistry University of Stuttgart

Sep 22, 2026, cs.LGJ/K move · Enter open · S save

One-Step Generative Surrogate Models via Block-Triangular Joint Drifting

Nicholas Geissler, Shreya Jha, Ricardo Baptista, Benjamin Peherstorfer

Courant Institute of Mathematical Sciences, New York University · Statistical Sciences, University of Toronto · Institute of Mathematics, EPFL

Surrogate Modeling One-Step Generative Modeling Conditional Generative Modeling Drifting Models

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Drifting provides a direct route to one-step generative models, but applying it directly to stochastic transition modeling requires multiple samples of the next state conditioned on the same current state. Standard trajectory data, however, typically provide only one realized next state for each observed current state and therefore do not provide an empirical approximation of the corresponding conditional distribution over possible next states. We introduce block-triangular joint drifting, which instead applies a projected drift field to the empirically accessible joint distribution of consecutive states. Importantly, the block-triangular architecture preserves the current-state marginal while making its second component a direct sampler of the conditional distribution of possible next states. The resulting surrogate generates stochastic trajectories with one model evaluation per time step, without auxiliary generative steps between time steps. Numerical experiments demonstrate accurate marginal and trajectory-dependent statistics and favorable accuracy-cost tradeoffs compared with deterministic, diffusion-, flow-, and distillation-based generative surrogate models.

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