CodaRAG improves RAG by using a CLS-inspired three-stage pipeline of knowledge consolidation, multi-dimensional associative navigation, and interference elimination, delivering 7-11% gains on GraphRAG-Bench for factual and reasoning tasks.
H.1.2 Information Extraction.Following LightRAG [ 6], we adopt a structured prompt to extract entities and binary relationships from text
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CodaRAG: Connecting the Dots with Associativity Inspired by Complementary Learning
CodaRAG improves RAG by using a CLS-inspired three-stage pipeline of knowledge consolidation, multi-dimensional associative navigation, and interference elimination, delivering 7-11% gains on GraphRAG-Bench for factual and reasoning tasks.