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A novel LLM framework that adapts inference strategies based on question type leads to superior performance in biomedical question answering, clinching first place in a competitive evaluation.
Merging image tokens intelligently can cut storage costs by over 16 times while boosting retrieval accuracy, challenging the notion that less data always means less context.
Early-stage reasoning failures can be drastically reduced from 64% to 13% with a novel RL approach that penalizes cascading errors in medical VQA.
