🤖 AI Summary
This work addresses the limitations of existing large language model (LLM)-based text clustering approaches, which typically rely on fixed pipelines that struggle to accommodate diverse corpus structures and cannot flexibly incorporate user-defined constraints such as desired cluster count or clustering intent. To overcome these challenges, the authors propose a dynamic multi-agent collaborative clustering framework, wherein a coordinator LLM adaptively orchestrates specialized agents—including proposers, synthesizers, reviewers, investigators, and critics—to construct controllable and context-aware text categorization systems. By replacing conventional static pipeline paradigms with a novel multi-agent dynamic decision-making mechanism, the method achieves state-of-the-art performance across seven public benchmarks, yielding up to a 32% relative improvement in Adjusted Rand Index (ARI) over the strongest LLM-based baseline.
📝 Abstract
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, and critic - adapting the pipeline to the corpus rather than executing a fixed one. On seven public text-clustering benchmarks the method achieves state-of-the-art performance, beating the strongest prior LLM baseline by up to 32% in ARI.