Cluster-R1: Large Reasoning Models Are Instruction-following Clustering Agents

• URL: http://arxiv.org/abs/2603.23518v1
• Date: Fri, 06 Mar 2026 10:15:54 GMT
• Title: Cluster-R1: Large Reasoning Models Are Instruction-following Clustering Agents
• Abstract summary: General-purpose embedding models excel at recognizing semantic similarities but fail to capture the characteristics of texts specified by user instructions.<n>We reframe instruction-following clustering as a generative task and train large reasoning models as autonomous clustering agents.<n>Our reasoning-driven training pipeline enables LRMs to interpret high-level clustering instructions and then infer the corresponding latent groupings.
• Score: 81.79110139097297
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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 training pipeline enables LRMs to interpret high-level clustering instructions and then infer the corresponding latent groupings. To evaluate this paradigm, we introduce ReasonCluster, a comprehensive benchmark comprising 28 diverse tasks spanning daily dialogue, legal cases, and financial reports. Experiments across diverse datasets and clustering scenarios show that our approach consistently outperforms strong embedding-based methods and LRM baselines, demonstrating that explicit reasoning fosters more faithful and interpretable instruction-based clustering.

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