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The article discusses ACORN's algorithm for fixing filtered vector search challenges and compares it with Qdrant's filterable HNSW method, including performance benchmarks on a million-point dataset.
This paper proposes replacing the proprietary GPT-3.5 metadata extractor in Multi-Meta-RAG with a lightweight, deterministic probe trained on hidden states of a small open-source model. The probe achieves 90.9% accuracy, outperforming GPT-3.5 (80.9%) and a substring baseline (88.0%), while avoiding allow-list drift and API costs.