State Representation Learning

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13 papers in the last four weeks, against 2 the four weeks before. 0.1% of all new papers.

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LASAR: Towards Spatio-temporal Reasoning with Latent Cognitive Map
May 16, 2026Jinzhou Tang, Sidi Liu, Waikit Xiu +2Spatiotemporal Reasoning State Representation Learning
1Sun Yat-sen University
Identify Then Project: Contrastive Learning of Latent Dynamics from Partial Observations with Port-Hamiltonian Structure
May 15, 2026Peilun Li, Kaiyuan Tan, Daniel Moyer +1Contrastive Learning Latent Dynamics Modeling
Department of Computer Science Vanderbilt University Nashville, TN 37235
Constrained latent state modeling: A unifying perspective on representation learning under competing constraints
May 15, 2026Gwenolé QuellecRepresentation Learning Predictive Representation Learning
LaTIM UMR 1101, Inserm, Brest, 29200, France.
Why Latent Actions Fail, and How to Prevent It
May 13, 2026Jung Min Lee, Taehyun Cho, Li Zhao +1Latent Action Learning Disentangled Representation Learning
Seoul National University · Microsoft Research
When Absolute State Fails: Evaluating Proprioceptive Encodings for Robust Manipulation
May 13, 2026Maxime Alvarez, Ryo Watanabe, Paul Crook +4Robot Policy Learning Generalization in Robotic Manipulation
TELEXISTENCE Inc, Foundation Model Division, Japan. · The University of Tokyo, Japan.
WriteSAE: Sparse Autoencoders for Recurrent State
May 12, 2026Jack YoungLanguage Model Steering Sparse Autoencoders
Indiana University
Policy Gradient Methods for Non-Markovian Reinforcement Learning
May 11, 2026Avik Kar, Siddharth Chandak, Rahul Singh +4Reinforcement Learning Policy Gradient Methods
Department of Computer Science and Automation, Indian Institute of Science, Bengaluru, India. · Department of Electrical Engineering, Stanford University, Stanford, CA, USA. · Department of Electrical and Electronics Engineering, Nanyang Technological University, Singapore. +1
MSPR: Multi-scale Predictive Representations for Goal-conditioned Reinforcement Learning
May 10, 2026Valliappan Chidambaram Adaikkappan, Sai Rajeswar, Pietro Mazzaglia +1Offline RL Predictive Representation Learning
Mila, McGill University · Qualcomm Research
Integrating Causal DAGs in Deep RL: Activating Minimal Markovian States with Multi-Order Exposure
May 8, 2026Jiamin Xu, Jacqueline Maasch, Kyra GanMarkov Decision Processes Causal Representation Learning
Cornell Tech, New York, NY
Hitting Time Isomorphism for Multi-Stage Planning with Foundation Policies
May 7, 2026Magnus Victor Boock, Abdullah Akgül, Mustafa Mert Çelikok +1Sequential Decision Making Offline RL
Department of Mathematics and Computer Science University of Southern Denmark
Semantic State Abstraction Interfaces for LLM-Augmented Portfolio Decisions: Multi-Axis News Decomposition and RL Diagnostics
May 7, 2026Likhita Yerra, Remi Uttejitha AllamExplainable Artificial Intelligence Quantitative Finance
AIVANCITY School of AI and Data · AIVANCITY School of AI & Data
Modular Reinforcement Learning For Cooperative Swarms
May 6, 2026Erel Shtossel, Gal A. KaminkaMulti-Robot Systems Reinforcement Learning
Bar Ilan University Ramat Gan, Israel
Latent State Design for World Models under Sufficiency Constraints
May 3, 2026Keon Woo KimRepresentation Learning Predictive Representation Learning
Om Labs
Lost in State Space: Probing Frozen Mamba Representations
Apr 30, 2026Bhagyashree Wagh, Akash SinghRepresentation Geometry Representation Collapse
University of Washington · University of Florida
The Bandit's Blind Spot: The Critical Role of User State Representation in Recommender Systems
Apr 29, 2026Pedro R. Pires, Gregorio F. Azevedo, Rafael T. Sereicikas +2User Modeling Recommender Systems
Federal University of São Carlos Sorocaba, São Paulo, Brazil
State Beyond Appearance: Diagnosing and Improving State Consistency in Dial-Based Measurement Reading
Apr 29, 2026Yuanze Hu, Gen Li, Yuqin Lan +5Multimodal Robustness Cross-Modal Alignment
Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Beihang University, Beijing, China · Beihang University, Beijing, China · Fudan University, Shanghai, China +1
Information bottleneck for learning the phase space of dynamics from high-dimensional experimental data
Apr 27, 2026K. Michael Martini, Eslam Abdelaleem, Paarth Gulati +1Dynamical Systems Information Bottleneck
Department of Physics, Emory University, Atlanta, GA, USA · Schools of Physics and Psychology, Georgia Institute of Technology, Atlanta, GA, USA · Initiative in Theory and Modeling of Living Systems, Emory University, Atlanta, GA, USA +1
Learning Dynamic Representations and Policies from Multimodal Clinical Time-Series with Informative Missingness
Apr 23, 2026Zihan Liang, Ziwen Pan, Ruoxuan XiongClinical Outcome Prediction Offline RL
Emory University, Atlanta, USA
Self-Predictive Representation for Autonomous UAV Object-Goal Navigation
Apr 22, 2026Angel Ayala, Donling Sui, Francisco Cruz +3UAV Navigation Predictive Representation Learning
Escola Polit´ecnica de Pernambuco, Universidade de Pernambuco, Recife, Brasil · School of Mechanical and Manufacturing Engineering, University of New South Wales, Sydney, Australia · Escuela de Ingenier´ıa, Universidad Central de Chile, Santiago, Chile
Hierarchical Active Inference using Successor Representations
Apr 17, 2026Prashant Rangarajan, Rajesh P. N. RaoActive Inference Hierarchical RL
Paul G. Allen Center for Computer Science and Engineering, University of Washington, Seattle, USA
JEPA-Bisim: Learning Robust Visual Representations for Planning with Joint-Embedding Predictive World Models
Feb 20, 2026Leonardo F. Toso, Davit Shadunts, Yunyang Lu +5Visual Representation Learning World Models for Robotics
Department of Electrical Engineering, Columbia University, New York, USA. · Capital One, USA.
Poincaré Meets Bellman: Revisable Memory, Operational Quotients, and Evidence-Supported Learning in Changing Environments
Nov 28, 2025Xin LiMarkov Decision Processes Dynamic Programming
Department of Computer Science University at Albany
Learning The Minimum Action Distance
Jun 10, 2025Lorenzo Steccanella, Joshua B. Evans, Özgür Şimşek +1Markov Decision Processes State Representation Learning
Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain · Department of Computer Science, University of Bath, Bath, United Kingdom
Decision-Centered Abstractions via Orthogonal Estimation of Difference-of-Q Functions
Jun 12, 2024Defu Cao, Angela ZhouValue Function Estimation State Abstraction
University of Southern California
TERC: A Transfer Entropy Redundancy Criterion for State Variable Selection in Reinforcement Learning
Jan 21, 2024Charles Westphal, Stephen Hailes, Mirco MusolesiReinforcement Learning State Representation Learning
UCL Centre for Artificial Intelligence Department of Computer Science University College London Gower Street, London WC1E 6BT, United Kingdom · Department of Computer Science University College London Gower Street, London WC1E 6BT, United Kingdom · Department of Computer Science and Engineering University of Bologna Viale Del Risorgimento 2, 40136 Bologna, Italy

May 16, 2026, cs.CVJ/K move · Enter open · S save

LASAR: Towards Spatio-temporal Reasoning with Latent Cognitive Map

Jinzhou Tang, Sidi Liu, Waikit Xiu, Weixing Chen+1

1Sun Yat-sen University

Spatiotemporal Reasoning State Representation Learning Memory-Augmented Agents

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A fundamental challenge in embodied AI is verifying if agents build internal models of spatial structure or merely learn to mimic task-specific expert trajectories. This is critical as foundational approaches rooted in action-centric tasks (e.g., VLN) and reasoning-centric tasks (e.g., EQA) often share a common limitation: they lack a learning signal that forces them to encode fine-grained spatial relationships (like topology or distance) over long-range, fragmented experiences. To address this, we first propose LASAR, an architecture featuring a dual-memory system designed to maintain both episodic experiences and a semantic cognitive map. We then introduce Spatio-temporal Contextual Representation Learning (ST-CRL), a contrastive objective designed to train this architecture. ST-CRL leverages spatio-temporal cues from cognitive queries generated through annotated spatio-temporal context in simulation to build sample pairs, thereby forming the internal cognitive map from the agent's experiences. Experiments demonstrate that our method achieves 2%-3.5% gains in both zero-shot generalization on standard VLN-CE and VSI-Bench benchmarks. We also demonstrate that our proposed cognitive map has high self-consistency.

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