residual-quantization

A paper proposing Residual Quantization (RQ) as a representation layer for contextual bandits, enabling nonlinear reward modeling with strictly bounded O(1) memory via gradient-boosted pseudo-targets and online depth adaptation through a shadow mechanism, beating non-RQ counterparts on 11 of 13 datasets while matching XGBoost and neural baselines at up to 1000x less memory.

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.