RAILS: Retrieval-Augmented Incremental LLM Clustering at Scale

Abstract: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 workloads require. We present RAILS, a retrieval-augmented incremental LLM clusterer that turns clustering into a simple loop over a growing label pool and scales through document batching with bounded concurrency. On six public benchmarks RAILS exceeds the strongest prior LLM-clustering method on average, lifting accuracy from 51.2% to 59.3%, NMI from 67.2% to 74.8%, and ARI from 45.4% to 54.7%. We further report production-deployment evidence from a SaaS ticket-topic-discovery pipeline, where RAILS has replaced a traditional HDBSCAN stage with higher clustering quality, transparent prompt-driven control, and stateful incremental operation.

Machine Learning (cs.LG)

arXiv:2609.24464 [cs.LG]

(or arXiv:2609.24464v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.24464 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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