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Forget tedious manual tuning: ScanHD lets robots autonomously configure laser profilers based on natural language instructions and visual context, achieving >92% accuracy in real-world inspection tasks.
Real-time glottis segmentation during Nasotracheal Intubation just got a whole lot faster and more accurate, thanks to a new network that's both lightweight and scale-robust.
By pretraining a VLA model with goal-conditioned RL, PRTS learns to reason about goal reachability, leading to substantial gains in long-horizon robotic tasks and zero-shot generalization.
LLM agents can now remember far more, far more accurately, by "seeing" their past experiences instead of just reading about them.
Forget ROUGE scores: QA-based evaluation finally offers interpretable feedback that lets summarization models self-improve without retraining.
LLM agent skills, despite their promise, often fail in realistic settings, with performance plummeting to no-skill baselines when agents must retrieve skills from a large, uncurated collection.
