Agentic Clustering: Controllable Text Taxonomies via Multi-Agent Refinement
Recent text-clustering methods use large language models to propose a cluster taxonomy from a corpus and then assign each text to it. These pipelines are fundamentally programmatic: the sequence of LLM calls and the rules for stopping, merging, and splitting clusters are fixed in code in advance, so they generalise poorly across corpora of different structure and cannot easily incorporate user-supplied constraints such as a target cluster count or a clustering intent. We propose an agentic alternative in which an orchestrator LLM inspects the state of the discovery process at each step and dispatches one of a small set of specialised agents -- proposer, synthesizer, auditor, investigator,...
| Authors | Simon Löwe, Emily Silcock |
|---|---|
| Primary category | cs.CL |
| Published | 31 May 2026 |
| Last updated at source | 9 October 2026 |
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