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GAFT transforms rare off-road failure data into robust hazard identification, boosting model performance by over 500% in critical scenarios.
Achieving 86.7% accuracy on direct commands, AdaHome outperforms LLM-based systems while slashing latency by up to 3x, all within a local deployment framework.
Achieving over 96% navigation success while reducing operational steps by nearly a third, this framework redefines efficiency in autonomous endovascular procedures.
LLMs can actively disentangle signal from noise in text-attributed graphs, leading to state-of-the-art performance in graph contrastive learning by shifting from random perturbation to semantic-aware disentanglement.