arXiv Machine Learning
RAILS: Retrieval-Augmented Incremental LLM Clustering at Scale
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RAILS: Retrieval-Augmented Incremental LLM Clustering at Scale
Using a Large Language Model (LLM) as the clusterer at production scale is hard: prompts cannot hold the entire label space, and per-document serial processing does not deliver the throughput real wor...
arXiv AI
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One Retrieval to Cover Them All: Co-occurrence-Aware Knowledge Base Reorganization for Session-Level RAG
arXiv:2606. 31156v1 Announce Type: cross Abstract: RAG systems retrieve documents optimized for answering one query at a time.
arXiv Machine Learning
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Efficient Clustering with Provable Guardrails for LLM Inference at Scale
arXiv:2607. 19704v1 Announce Type: new Abstract: Scaling LLM-based applications to millions of users is bottlenecked by the inference cost and latency of modern foundation models.
arXiv Computation and Language
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Incremental Pooled LLM Evaluation for Cost-Effective Retrieval Model Selection
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LiteRAG: Cost-Efficient Graph-Based Retrieval-Augmented Generation
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arXiv Machine Learning
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Diagnosing and Mitigating Retrieval Bottlenecks in LLM-Based Cold-Start Recommendation
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