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This paper proposes a retrieval-grounded small language model framework that uses formal concept analysis as a symbolic verification loop for ontology construction, demonstrating its effectiveness in a rare ataxia setting.
ContextRAG introduces an extraction-free method for constructing hierarchical graph indices for retrieval-augmented generation, using Residual-Quantization K-Means and Formal Concept Analysis to reduce LLM calls and tokens by orders of magnitude while maintaining competitive F1 scores on multi-hop questions.