residual-quantization

PRQ-KMeans is a post-hoc tokenization method that improves residual quantization for semantic identifiers by removing global-mean components, refining centroids, and using projection residuals, achieving significant performance gains in industrial search and public recommendation benchmarks.

Introduces Recurrent Residual Quantization (RRQ), a post-training quantization framework that enables multiple effective precisions from a single LLM checkpoint via additive residual refinement, improving flexibility and construction speed.

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.