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Fast-LeWM accelerates visual planning by replacing autoregressive rollout with parallel action-prefix prediction, reducing computational costs and latency accumulation during long-horizon predictions.
This paper introduces OmniManim, a render-feedback-aware framework for generating educational animations from natural language descriptions using large language models. It addresses visual defects like element overlap and misalignment by incorporating explicit visual planning, post-render diagnostics, and localized repair, demonstrating improved render quality on newly constructed datasets.