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Scheduling imagination in VLA models can cut GPU costs by 80% while boosting performance and robustness in real-world tasks.
Temporal updates in LLMs can be made without sacrificing historical accuracy, achieving over 23% improvement in consistency with a single optimized representation.
SVP-IL boosts success rates on ambiguous language tasks by over 60% with minimal training data, revolutionizing data efficiency in robotic manipulation.
Freezing a Stable Diffusion backbone and injecting CLIP and BLIP features lets you beat the state-of-the-art in zero-shot sketch-based 3D shape retrieval, without any costly retraining.
MV-HGNN achieves superior 3D shape retrieval by effectively leveraging geometric dependencies and semantic alignment, outperforming existing methods in zero-shot settings.
