HaM-World: Soft-Hamiltonian World Models with Selective Memory for Planning

arXiv:2605.05951 · cs.AI · Submitted 2026-05-07 · Read on arXiv

cs.AI

Submitted: 2026-05-07

Updated: 2026-09-25

Code: https://github.com/HaoyunT/HaM_World

Terminology

• World Model — 36× in this paper · explained in 7 episodes
• Latent Dynamics — 26× in this paper · explained in 2 episodes
• Reinforcement Learning — 14× in this paper · explained in 4 episodes
• AUC — 13× in this paper · explained in 2 episodes
• Alignment — 13× in this paper · explained in 1 episode
• Consistency — 13× in this paper · explained in 3 episodes
• Actor — 12× in this paper · explained in 3 episodes
• Ablation — 9× in this paper · explained in 1 episode
• Core — 6× in this paper · explained in 2 episodes
• Regularization — 6× in this paper · explained in 3 episodes
• Transformer — 6× in this paper · explained in 2 episodes
• Generalization — 4× in this paper · explained in 13 episodes
• Distribution Shift — 3× in this paper · explained in 6 episodes
• Sparsity — 3× in this paper · explained in 4 episodes
• Trace — 3× in this paper · explained in 2 episodes
• Ablation study — 2× in this paper · explained in 1 episode
• Robustness — 2× in this paper · explained in 5 episodes
• Sample Efficiency — 2× in this paper · explained in 4 episodes
• Self-Supervised Learning — 2× in this paper · explained in 8 episodes
• Abstraction — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• AdamW Optimizer — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Conservation Laws — 1× in this paper · explained in 3 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• Deep Reinforcement Learning — 1× in this paper · explained in 2 episodes
• Exposure Bias — 1× in this paper · explained in 2 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Latent Representation — 1× in this paper · explained in 2 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Parameter Efficiency — 1× in this paper · explained in 3 episodes
• Structural Constraints — 1× in this paper · explained in 2 episodes
• Temporal Abstraction — 1× in this paper · explained in 2 episodes
• Zero-Shot Generalization — 1× in this paper · explained in 5 episodes

Sources

• DeepMind Control Suite
• Proximal Policy Optimization Algorithms
• V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning

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