FD-RAG learns semantic-aware adaptive hypergraphs over local corpora, distills them into compact QA memories, answers covered queries via memory matching, invokes LLM reasoning only when needed, and aggregates anonymized memories federatedly, claiming up to 7.8% higher accuracy and 8.4× lower latenc
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OIDA is a proposed framework that represents organizational knowledge as epistemic Knowledge Objects with class-specific importance decay and signed contradictions, plus a QUESTION mechanism that surfaces modeled ignorance via inverse decay.
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FD-RAG: Federated Dual-System Retrieval-Augmented Generation
FD-RAG learns semantic-aware adaptive hypergraphs over local corpora, distills them into compact QA memories, answers covered queries via memory matching, invokes LLM reasoning only when needed, and aggregates anonymized memories federatedly, claiming up to 7.8% higher accuracy and 8.4× lower latenc
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Retrieval Is Not Enough: Why Organizational AI Needs Epistemic Infrastructure
OIDA is a proposed framework that represents organizational knowledge as epistemic Knowledge Objects with class-specific importance decay and signed contradictions, plus a QUESTION mechanism that surfaces modeled ignorance via inverse decay.