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LaST outperforms traditional models by harnessing the strengths of both large and small architectures, achieving unprecedented accuracy in zero-shot surgical phase recognition.
Dynamic integration of multimodal knowledge boosts the recognition of rare surgical actions, overcoming critical optimization conflicts in robot-assisted surgery.
Static knowledge isn't enough; LLM agents achieve only a 72.6% success rate in executing complex aviation tasks, revealing significant gaps in procedural execution.
Distributing SciML models with hardware and physics awareness slashes latency and energy consumption by over 8x and 33x, respectively, while paradoxically *improving* reconstruction fidelity.
