GraphDC applies divide-and-conquer multi-agent LLM reasoning to graph algorithms by decomposing graphs into subgraphs for local agents and integrating via a master agent, outperforming direct methods especially on large scales.
Improving hospital risk prediction with knowledge- augmented multimodal ehr modeling
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EHR-RAGp is a retrieval-augmented EHR foundation model that employs prototype-guided retrieval to dynamically integrate relevant historical patient context, outperforming prior models on clinical prediction tasks.
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GraphDC: A Divide-and-Conquer Multi-Agent System for Scalable Graph Algorithm Reasoning
GraphDC applies divide-and-conquer multi-agent LLM reasoning to graph algorithms by decomposing graphs into subgraphs for local agents and integrating via a master agent, outperforming direct methods especially on large scales.
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EHR-RAGp: Retrieval-Augmented Prototype-Guided Foundation Model for Electronic Health Records
EHR-RAGp is a retrieval-augmented EHR foundation model that employs prototype-guided retrieval to dynamically integrate relevant historical patient context, outperforming prior models on clinical prediction tasks.