Title: HaM-World: Soft-Hamiltonian World Models with Selective Memory for PlanningSource: http://arxiv.org/abs/2605.05951v1

Summary:

This paper introduces a foundational architectural primitive for world models by combining Hamiltonian geometric structures with Mamba-based selective memory to stabilize long-horizon planning. It provides agents with a structured latent state for dynamics, rewards, and action search, significantly improving robustness in out-of-distribution planning tasks.