DeformGen uses dynamics-based topological augmentation to generate diverse deformable object states and warp trajectories for improved manipulation policy learning.
AMS replaces global token eviction with adaptive region-aware KV quotas to prevent reasoning block wipe-out, boosting long-context performance without extra attention overhead.
Generation Navigator is a state-aware multi-turn text-to-image agent that learns to steer generation via trajectory-level reinforcement learning, achieving a 0.90 WISE score and 79.06% reasoning accuracy.
PILA injects physics-structured latent guidance into frozen video generators via mixture-of-experts alignment, achieving state-of-the-art physical plausibility and visual quality.