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A small fraction of sharp dimensions governs the generalization of PEFT methods, and optimizing these can lead to substantial performance gains.
GUI-AC transforms how GUI agents learn by stabilizing their performance amidst the chaos of non-stationary environments.
Current multimodal LLMs struggle with guideline-constrained clinical reasoning, but a simple multi-agent framework can significantly boost their performance on real-world lung cancer diagnosis and treatment.
