AI RESEARCH
Cluster-R1: Large Reasoning Models Are Instruction-following Clustering Agents
arXiv CS.CL
•
ArXi:2603.23518v1 Announce Type: new General-purpose embedding models excel at recognizing semantic similarities but fail to capture the characteristics of texts specified by user instructions. In contrast, instruction-tuned embedders can align embeddings with textual instructions yet cannot autonomously infer latent corpus structures, such as determining the optimal number of clusters. To address both limitations, we reframe instruction-following clustering as a generative task and train large reasoning models (LRMs) as autonomous clustering agents. Our reasoning-driven