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Energy-efficient underwater vehicle control can be achieved without manual weight tuning, reducing power consumption by up to 65% while preserving task performance.
LLMs can learn to intelligently route evidence across modalities to improve recommendations when data is missing, outperforming deterministic fusion methods by a significant margin.
Automating turbomachinery design is now possible: an LLM-orchestrated agent team can generate and optimize compressor designs from natural language requirements in just 30 minutes.
Achieve up to 12% improvement in Rouge-LSum for multimodal tasks in edge-cloud settings by jointly training heterogeneous edge models with a server model, even with varying modality availability.
