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Forget PEFT and KD, reprogramming distillation offers a surprisingly effective and robust way to adapt large medical foundation models to diverse downstream tasks.
Training a multimodal agent from scratch beats retrofitting existing LMMs with search tools, especially when you compress long interaction histories into visual summaries.
Current Composed Image Retrieval benchmarks are misleading, as a new evaluation reveals that models struggle with query ambiguity and interactive scenarios.
Injecting rare disease knowledge into data synthesis and using self-supervised RL on pseudo-labels dramatically improves medical reasoning in LLMs, outperforming existing methods by up to 5.93% on rare disease tasks.
