WMAttack automates finite-budget attack search for world-model agents via SCAS and RGAR, reporting higher normalized reward drops than baselines on Atari and DMC tasks.
Dream to control: Learning behaviors by latent imagination
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ZALT learns latent hub states and hub-to-hub dynamics from demonstrations to plan zero-shot solutions for unseen start-goal tasks, achieving 55% success in a 3D maze versus 6% for baselines.
MolWorld expands a molecule-transfer graph using a world model to discover high-property molecules that maintain strong structural connectivity to known compounds for actionable optimization.
HaM-World integrates soft-Hamiltonian dynamics with selective state-space memory to reduce long-horizon rollout error by 55% and achieve top returns under 12 OOD perturbations on DeepMind Control Suite tasks.
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HaM-World: Soft-Hamiltonian World Models with Selective Memory for Planning
HaM-World integrates soft-Hamiltonian dynamics with selective state-space memory to reduce long-horizon rollout error by 55% and achieve top returns under 12 OOD perturbations on DeepMind Control Suite tasks.