Efficient Clustering with Quality Guardrails for LLM-based Recommender Systems at Industry Scale
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arXiv:2607.19704v2 Announce Type: replace-cross Abstract: LLMs can be prohibitively expensive and slow to run at scale, especially for applications that invoke an LLM per sample over millions of inputs. A natural way to scale is to cluster the inputs, run the LLM only on cluster representatives, an…
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- 2026-09-07 04:00 · arXiv stat.ML
Efficient Clustering with Quality Guardrails for LLM-based Recommender Systems at Industry Scale