Title: Agent-Centric Personalized Multiple Clustering with Multi-Modal LLMs
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: Personalized Multiple Clustering, Multi-Modal LLMs, Agent

Abstract: Personalized multiple clustering aims to generate diverse partitions of a dataset based on different user-specified aspects, rather than a single clustering. It has recently drawn research interest for accommodating varying user clustering preferences.
Recent approaches primarily use CLIP embeddings with proxy learning to extract representations biased toward user interests. However, CLIP primarily focuses on coarse image-text alignment, lacking a deep contextual understanding of user interests.
To overcome these limitations, we propose an agent-centric personalized clustering framework that leverages multi-modal large language models (MLLMs) as agents to comprehensively traverse a relational graph to search for clusters based on user interests. Due to the advanced reasoning mechanism of MLLMs, the obtained clusters align more closely with user-defined criteria than those obtained from CLIP-based representations. To reduce computational overhead, we shorten the agents' traversal path by constructing a relational graph using user-interest-biased embeddings extracted by MLLMs. 
A large number of weak edges can be filtered out based on embedding similarity, facilitating an efficient traversal search for agents. Experimental results show that the proposed method achieves NMI scores of 0.9667 and 0.9481 on the Card Order and Card Suits benchmarks, respectively, largely improving the SOTA model by over 140%. Code is available at \url{https://anonymous.4open.science/r/Agent-Centric-Clustering-BEA5/}.