SRL-MPC integrates reinforcement-learned parameter updates with shape-aware model predictive control via geometric separation features to navigate dense heterogeneous robot crowds safely and adaptively.
PARE combines structure-aware width pruning and timestep-conditioned adaptive depth routing to cut video diffusion compute while preserving generation quality.
LoRA's scaling factor dominates optimization by amplifying task signals without increasing drift, outperforming learning rate adjustments. The optimal alpha follows a sublinear square-root law with rank, revealing insufficient scaling in existing heuristics. Proposed LoRA-alpha restores principled s