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Bridging the gap between proprietary and open-source models, MAPD achieves up to 44.4% success in QA tasks by transforming sparse RL signals into dense distillation guidance.
Current reranking methods fall short, but Rubric4Setwise transforms evaluation into actionable selection signals, achieving unprecedented performance across diverse document sets.
Current multimodal large language models struggle with OCT image understanding, falling short even with specialized adaptations.
