mlsanityData to 7 Oct 2026

Time-Aware World Model for Adaptive Prediction and Control

arXiv 2506.08441 ↗First posted 10 Jun 2025cs.LG cs.AI cs.SYcited by 4

On the map: World models → Latent predictive world models

Abstract (arXiv)

In this work, we introduce the Time-Aware World Model (TAWM), a model-based approach that explicitly incorporates temporal dynamics. By conditioning on the time-step size, Deltat, and training over a diverse range of Deltat values -- rather than sampling at a fixed time-step -- TAWM learns both high- and low-frequency task dynamics across diverse control problems. Grounded in the information-theoretic insight that the optimal sampling rate depends on a system's underlying dynamics, this time-aware formulation improves both performance and data efficiency. Empirical evaluations show that TAWM consistently outperforms conventional models across varying observation rates in a variety of control tasks, using the same number of training samples and iterations. Our code can be found online at: github.com/anh-nn01/Time-Aware-World-Model.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Placed in Latent predictive world models, one of 11 directions in World models. Builds on 28 of its 44 references that are in the map. First posted on arXiv 10 Jun 2025.

Builds on

28 of 44 references.

Closest ancestors · 1

Same direction, newest first

  1. 2022Temporal Difference Learning for Model Predictive Control9 Mar

Other references · 27

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,635
  3. 2015Trust Region Policy Optimizationcited by 2,503
  4. 2019Dream to Control: Learning Behaviors by Latent Imaginationcited by 1,598
  5. 2018World Modelscited by 1,444
  6. 2023Mastering Diverse Domains through World Modelscited by 1,193
  7. 2019Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learningcited by 1,168
  8. 2020Mastering Atari with Discrete World Modelscited by 990
  9. 2019When to Trust Your Model: Model-Based Policy Optimizationcited by 753
  10. 2020Deep Reinforcement Learning for Autonomous Driving: A Surveycited by 698
  11. 2019Model-Based Reinforcement Learning for Ataricited by 495
  12. 2023TD-MPC2: Scalable, Robust World Models for Continuous Controlcited by 482
15 more
  1. 2022DayDreamer: World Models for Physical Robot Learningcited by 372
  2. 2018Residual Reinforcement Learning for Robot Controlcited by 242
  3. 2021Pathdreamer: A World Model for Indoor Navigationcited by 117
  4. 2022Accelerated Policy Learning with Parallel Differentiable Simulationcited by 92
  5. 2022Do Differentiable Simulators Give Better Policy Gradients?cited by 83
  6. 2024World Models for Autonomous Driving: An Initial Surveycited by 79
  7. 2019PIPPS: Flexible Model-Based Policy Search Robust to the Curse of Chaoscited by 56
  8. 2017A Reinforcement Learning Approach to Weaning of Mechanical Ventilation in Intensive Care Unitscited by 50
  9. 2022Investigating Compounding Prediction Errors in Learned Dynamics Modelscited by 47
  10. 2021Learning Dynamics Models for Model Predictive Agentscited by 21
  11. 2023Gradient Informed Proximal Policy Optimizationcited by 8
  12. 2023Multi Time Scale World Modelscited by 7
  13. 2023Controlgym: Large-Scale Control Environments for Benchmarking Reinforcement Learning Algorithmscited by 4
  14. 2022A Sampling Theorem for Exact Identification of Continuous-time Nonlinear Dynamical Systemscited by 2
  15. 2024Physics-Guided Reinforcement Learning System for Realistic Vehicle Active Suspension Controlcited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 4.

4 papers.

  1. 2026OpenWorldLib: A Unified Codebase and Definition of Advanced World Modelscited by 3
  2. 2026Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoningcited by 1
  3. 2026OpenVO: Open-World Visual Odometry with Temporal Dynamics Awarenesscited by 0
  4. 2026Temporal Self-Imitation Learningcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024PWM: Policy Learning with Multi-Task World Models
  2. 2026World Models Under Asynchronous Sensor Observations
  3. 2026RealtimeWAM: How Fast Can I Run My World Action Model?
  4. 2025World Models as Reference Trajectories for Rapid Motor Adaptation
  5. 2026DWM: Separating World Effects from Actions in Latent World Models
  6. 2025Trajectory World Models for Heterogeneous Environments
  7. 2026OnlineWM: Causality-Aware Active Online Learning for Effective World Modeling
  8. 2026From Gradients to Riccati Geometry: Kalman World Models for Single-Pass Learning