Mini-Batch K-Means followed by greedy set-cover within each bucket guarantees every sample lands with a representative that is at least α-similar and attribute-identical, reducing LLM inference cost ~50× at 38M-customer scale.
Static and dynamic information organization with star clusters
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Efficient Clustering with Provable Guardrails for LLM Inference at Scale
Mini-Batch K-Means followed by greedy set-cover within each bucket guarantees every sample lands with a representative that is at least α-similar and attribute-identical, reducing LLM inference cost ~50× at 38M-customer scale.