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Explicit multi-timescale predictions in LM-X not only enhance robot manipulation success rates but also provide intrinsic explanations for control decisions, transforming how we understand robotic action generation.
MemCatalyst reveals that targeted data poisoning can drastically boost membership inference accuracy in Vision-Language Models with minimal resource expenditure.
Tactile feedback can enhance VLA models without compromising their pretrained knowledge, leading to a 30.6 percentage-point improvement in manipulation tasks.
Privacy risks in retrieval-augmented generation are not static; they vary dynamically with user queries, and our new framework addresses this critical oversight.
GeoHAT achieves a remarkable 79.3% success rate in mobile manipulation, outperforming existing methods by a staggering 23.7% through innovative geometric token integration.
