Temporal Representation Learning

Latest papers

47 papers16 papers in the last four weeks, up 100% on the four weeks before. 0.1% of all new papers.

Temporal Forcing: 4D Representation Alignment for Vision-Language-Action Models
Aug 31, 2026Xingyu Ding, Yuzhong Zhao, Chunhai Zhao +3Representation Learning Temporal Representation Learning
Nanjing University, Nanjing, China · Institute of Automation, Chinese Academy of Sciences, Beijing, China · University of Chinese Academy of Sciences, Beijing, China
See the Change, Keep the Flow: Unsupervised Action Segmentation via Spectral-Temporal Representation Learning
Aug 30, 2026Yun Li, Jun Xiao, Cong Zhang +1Temporal Action Segmentation Temporal Representation Learning
School of Control Science and Engineering, Shandong University
Composed Historical Image Retrieval by Modeling Temporal Representations
Aug 19, 2026Adrià Molina Rodríguez, Oriol Ramos Terrades, Josep Lladós CanetTemporal Representation Learning Representation Learning
Centre de Visió per Computador Universitat Autònoma de Barcelona Bellaterra, Catalonia · Computer Science Department Universitat Autònoma de Barcelona, Bellaterra, Catalonia
ChronoSSM: Training for Temporally Aware Representations in Autoregressive State Space Models
Aug 10, 2026Adrien Schoen, Nachiketa Ratnakar Patil, Arjun Bhagoji +1Representation Learning Temporal Representation Learning
Institut universitaire de France
TimePLE: Rethinking Temporal Representation for Video Temporal Grounding
Jul 27, 2026Yuhui Zeng, Xinyu Mao, Xiaokun Liu +4Temporal Video Grounding Representation Learning
Media Analytics and Computing Lab, Xiamen University, Xiamen, China · Kling Team, Kuaishou Technology, China · The Chinese University of Hong Kong, HKSAR, China +1
You Don't Need Strong Assumptions: Visual Representation Learning via Temporal Differences
Jun 14, 2026Ninad Daithankar, Alexi Gladstone, Yann LeCun +1Visual Representation Learning Video Representation Learning
1UIUC · 2New York University
Contrast encodes inductive bias: separating slow noise from dynamics in predictive representation learning
Jun 5, 2026Paarth Gulati, Ilya NemenmanContrastive Learning Representation Learning
Department of Physics, and Initiative in Theory and Modeling of Living Systems, Emory University, Atlanta, GA
Stateful Visual Encoders for Vision-Language Models
Jun 3, 2026Zirui Wang, Junwei Yu, Adam Yala +3Vision-Language Models Visual Representation Learning
1Voio, Inc. · 2UC Berkeley · 3UCSF
Giving Sensors a Voice: Multimodal JEPA for Semantic Time-Series Embeddings
May 29, 2026Utsav Dutta, Gerardo Pastrana, Sina Khoshfetrat Pakazad +1Multivariate Time Series Forecasting Joint-Embedding Predictive Architecture
C3 AI, Redwood City, CA, USA
STEP: Learning STructured Embeddings for Progressive Time Series
May 29, 2026Lucas Thil, Jesse Read, Rim Kaddah +1Self-Supervised Time Series Representation Learning Interpretable ML
LIX, École Polytechnique Palaiseau, France · IRT SystemX Palaiseau, France · Safran Tech Chateaufort, France
The TIME Machine: On The Power of Motion for Efficient Perception
May 21, 2026Mantas Skackauskas, Xinyue Hao, Laura Sevilla-LaraMotion Representation Learning Self-Supervised Visual Representation Learning
School of Informatics University of Edinburgh
Foresee-to-Ground: From Predictive Temporal Perception to Evidence-Driven Reasoning for Video Temporal Grounding
May 21, 2026Zelin Zheng, Xinyan Liu, Ruixin Li +4Temporal Video Grounding Temporal Representation Learning
School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, China · Beijing Key Laboratory of Embodied Intelligence Computing, Beijing, China · Faculty of Computing, Harbin Institute of Technology, Weihai, China +1
Divide and Contrast: Learning Robust Temporal Features without Augmentation
May 20, 2026Abdul-Kazeem Shamba, Kerstin Bach, Gavin TaylorContrastive Learning Self-Supervised Learning
Department of Computer Science, Norwegian University of Science and Technology, Norway · Department of Computer Science, United States Naval Academy, USA.
Tracing the Arrow of Time: Diagnosing Temporal Information Flow in Video-LLMs
May 8, 2026Peitao Han, Fei Cheng, Lis K. Pereira +2Video-Language Models Temporal Reasoning in Language Models
The University of Osaka · Center for Information and Neural Networks · National Institute of Information and Communications Technology +2
Exploring Temporal Representation in Neural Processes for Multimodal Action Prediction
Apr 9, 2026Marco Gabriele Fedozzi, Yukie Nagai, Francesco Rea +1Temporal Representation Learning Robotics
DIBRIS Department, University of Genoa, Via All’Opera Pia 13 16145 Genoa, Italy · CONTACT Unit, Italian Institute of Technology, Via Enrico Melen 83, 16152 Genoa, Italy · International Research Center for Neurointelligence, The University of Tokyo 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
It's a Matter of Time: Three Lessons on Long-Term Motion for Perception
Feb 16, 2026Willem Davison, Xinyue Hao, Laura Sevilla-LaraMotion Representation Learning Temporal Video Understanding
University of Edinburgh
SPIRE: Shared-Private Representation Learning for Continuous Multi-Region LFP under Perturbation
Oct 28, 2025Rahil Soroushmojdehi, Sina Javadzadeh, Mehrnaz Asadi +1Shared Representation Learning Disentangled Representation Learning
Department of Electrical Engineering and Computer Science · Department of Biomedical Engineering, University of California, Irvine, CA, USA · Department of Neurology, Children’s Health Orange County, Orange, CA, USA

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Temporal Forcing: 4D Representation Alignment for Vision-Language-Action Models

Xingyu Ding, Yuzhong Zhao, Chunhai Zhao, Yinghuan Shi+2

Nanjing University, Nanjing, China · Institute of Automation, Chinese Academy of Sciences, Beijing, China · University of Chinese Academy of Sciences, Beijing, China

Representation Learning Temporal Representation Learning Robotic Manipulation Vision-Language-Action Models Latent Space Alignment Temporal Modeling for VLA Models

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Long-horizon robotic manipulation requires vision-language-action (VLA) models to track scene states and their evolution beyond the current observation. However, simply conditioning policies on observation history does not guarantee that the history is effectively utilized: action supervision constrains what the policy should do, but only indirectly constrains what its history representations should retain. To resolve this, we present Temporal Forcing, a 4D representation alignment framework that explicitly supervises latent temporal states and their transitions. Specifically, we first introduce a history pathway that compresses past observations into compact latent tokens. We then align these tokens and current-frame features with geometric targets from a pretrained 4D foundation model, providing direct supervision at both the state and transition levels. The 4D foundation model and alignment heads are used only for training-time supervision. Temporal Forcing improves average success from 96.6% to 98.8% on LIBERO, with the largest gain on LIBERO-Long (93.8% to 97.2%), and from 53.5% to 62.8% across twelve RoboTwin 2.0 tasks. Furthermore, Temporal Forcing increases full-task success from 20.0% to 43.3% on a physical multi-stage hidden-placement task. Controlled experiments show that 4D representation alignment is crucial for making observation history beneficial to the model. Code will be publicly available.