Title: LLM-DAMVC: A Large Language Model Assisted Dynamic Agent for Multi-View Clustering
Venue: NeurIPS 2025
Accepted: yes
Keywords: Multi-View Clustering
Abstract: Multi-view clustering integrates the consistency and complementarity of different views to achieve unsupervised data grouping. Existing multi-view clustering methods primarily confront two challenges: i) they generally perform feature extraction in the feature domain, which is sensitive to noise and may neglect cluster-specific information that is indistinguishable in the original space; ii) current dynamic fusion methods adopt static strategies to learn weights, lacking capability to adjust strategies adaptively under complex scenarios according to variations in data distribution and view quality. To address these issues, we propose a large language model assisted dynamic agent for multi-view clustering (LLM-DAMVC), a novel framework that recasts multi-view clustering as a dynamic decision-making problem orchestrated by a large language model. Specifically, each view is equipped with complementary agents dedicated to feature extraction. A dual-domain contrastive module is introduced to optimize feature consistency and enhance cluster separability in both the feature domain and frequency domain. Additionally, an LLM-assisted view fusion mechanism provides a flexible fusion weight learning strategy that can be adaptively applied to complex scenarios and significantly different views. Extensive experimental results validate the effectiveness and superiority of the proposed method.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nthis work introduces a large-language model guided dynamic agent multi-view clustering. Overall, the integration of a large language model (LLM) into the learning process of multi-view clustering is interesting. However, there are several serious issues in this work.\n\n1. The motivation of this work is not convincing. for many tasks (not only multi-view clustering), they all excessive reliance on feature extraction. From my perspective, the primary objective of multi-view clustering should be to fully exploit the information across multiple views and uncover the underlying correlations between them, rather than focusing solely on feature extraction. Regarding the second motivation, there already exist numerous studies that incorporate dynamic fusion strategies into the multi-view clustering process.\n2. The authors claim that \"static or semi-static fusion strategies exhibit limited adaptability when facing drastic view-quality variations at the sample level.\" However, this argument is not sufficiently supported. More concrete examples or experimental evidence should be provided to substantiate this claim.\n3. For the view-quality, it should be provided a detailed definition. Actually, the evaluation of view quality is closely related to the clustering methods employed. For example, view A maybe better than view B when using k-means algorithm, while view A maybe worse than view B when using subspace clustering.\n4. The considering of exploring information from frequency domain is interesting. The authors claim that \"static or semi-static fusion strategies exhibit limited adaptability when facing drastic view-quality variations at the sample level.\" However, this argument is not sufficiently supported. More concrete examples or experimental evidence should be provided to substantiate this claim.\n5. The overall comparison is both insufficient and potentially biased. Detailed descriptions of the datasets used are not provided, and all selected datasets are relatively small-scale. Moreover, several state-of-the-art multi-view clustering methods—such as low-rank tensor-based approaches (e.g., t-SVD-MSC, TLpNM-MSC)—should also be included as baseline methods for fair comparison.\n6. Several typos, such as “Contrastive”-->” contrastive” in page 1 line 13, and “v” in Eq. (1).\n\nSTRENGTHS AND WEAKNESSES:\nPlease refer to \"Summary\"\n\nQUESTIONS:\nPlease refer to \"Summary\"\n\nLIMITATIONS:\nPlease refer to \"Summary\"", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-06-20"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper develops a large language model guided dynamic agent multi-view clustering method, which introduces the reasoning capabilities of large-scale language model (LLM) to enhance the performance of the traditional clustering models. Specifically, each view is processed by two complementary agents: a stable feature extraction agent and an adversarial robustness agent, followed by a Dual-Domain Contrastive Learning module that enforces consistency in both the spatial and frequency domains. An LLM-Assisted Dynamic Feature Aggregation (LDFA) module then computes adaptive fusion weights via an LLM-based policy network to integrate view-specific embeddings. The authors also perform several experiments and the corresponding analyses to support the conclusion of the work.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths of the paper are: \n(1) Reframing multi-view clustering as a dynamic decision-making problem and introducing an LLM as the \"decision brain\" is a highly innovative concept. It offers a promising solution to the limitations of static or semi-static fusion strategies in traditional MVC methods.\n(2) The experimental section of the paper is solid. LLM-DAMVC achieves superior performance on multiple datasets, which supports the authors' claims. \n(3) The entire narrative, from problem definition and methodology to experimental analysis, is easy to follow. Figure 1 provides an intuitive visualization of the overall architecture, which helps readers quickly grasp the core ideas of the model.\n\nWeaknesses of the paper are:\n(1) Adversarial agents rely on pseudo labels generated by themselves to construct adversarial samples. In the early stages of clustering tasks or when dealing with noisy data, the quality of pseudo labels may be low, which might lead to potential negative influence on performance.\n(2) The authors adopt heterogeneous agents for multi-view data, but its justifications and significance are not straightforward.\n\nQUESTIONS:\n(1) Have you considered applying more sophisticated clustering algorithms (e.g., k-means or Gaussian mixture models) on \\( \\mathcal{Z} \\) to better exploit its inherent cluster structures? \n(2) In clustering, the quality of pseudo labels cannot be guaranteed. Have you considered any countermeasure in the model design or training strategy to mitigate the negative effects of low-quality pseudo labels?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 4, "ORIGINALITY": 3, "DATE": "2025-06-25"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents LLM-DAMVC, a novel multi-view clustering (MVC) framework that integrates a Large-scale Language Model (LLM) to enhance the clustering process. LLM-DAMVC introduces three key innovations: a multi-agent collaboration module that extracts more robust and distinctive features using different agents; a dual-domain contrastive learning module that employs Fast Fourier Transform (FFT) to simultaneously capture local details and global structure; and an LLM-guided dynamic feature aggregation mechanism that adaptively assigns fusion weights based on real-time evaluations of view quality.\n\nSTRENGTHS AND WEAKNESSES:\n- Strengths\n\t1. This paper introduces an innovative use of Large Language Models (LLMs) to support multi-view feature fusion in MVC tasks, offering a fresh approach to addressing core challenges in the field.\n\t2. The overall design is well-structured and technically sound, demonstrating a thoughtful integration of multiple components.\n\t3. Extensive experiments and comparative studies demonstrate the effectiveness of the proposed method.\n- Weaknesses\n\t1. Incorporating LLMs into MVC may introduce substantial computational overhead, raising questions about the practical necessity of using LLMs in this context.\n\t2. The dependence of the FFT-based frequency-spectral contrastive learning on specific data types (e.g., texts or signals) needs to be further clarified. Its generalization ability also needs to be discussed.\n\nQUESTIONS:\n1. What are the specific advantages of using a Large Language Model (LLM) for dynamic weight assignment, especially when compared to traditional attention mechanisms or the lightweight policy network discussed in the paper?\n2. In what ways does frequency-domain analysis enhance performance for general high-dimensional feature vectors, particularly when the data is not derived from signals?\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-25"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents LLM-DAMVC, a novel multi-view clustering framework that deploys heterogeneous agents for each view to perform initial feature extraction. The proposed method incorporates dual-domain contrastive learning, encompassing both the feature domain and the frequency domain. It then leverages large language models (LLMs) for dynamic view-quality assessment and attention-based fusion. Extensive experiments on six benchmark datasets demonstrate significant improvements over existing methods.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\nS1. This is the first study to integrate large language models (LLMs) as a decision-making module for adaptive view fusion, thereby effectively overcoming the limitations of static strategies.\nS2. The dual-domain contrastive module integrates spatial similarity and spectral structural patterns, which contributes to enhancing robustness against noise.\nS3. Comprehensive evaluations were conducted on 6 datasets, and the results effectively demonstrate the superiority of the proposed method.\n\nWeaknesses:\nW1. LLM has been proposed and applied for a few years, and the authors conduct a wider range of literature review and performs a detailed analysis of its application in clustering, so as to demonstrate the novelty of the work.\nW2. The work adopts LLM as decision making module, and does the method requires training or fine tuning of LLM? For another, how about the different LLMs influence the clustering performance should be further analyzed.\nW3. There are some typographical errors, which should be corrected to improve overall clarity.\n\nQUESTIONS:\nQ1. Current evaluations of \"view quality\" primarily focus on prediction certainty. Nevertheless, a high-quality view can also be defined by its complementarity to other views and its noise level. Can the evaluation of view quality reflect complementarity or noise level?\n\nQ2. Large language models (LLMs) are invoked at specific intervals, generating relatively static strategies. Could this design fail to capture the batch-level dynamics of data? Why not adopt more frequent LLM-driven decisions to achieve finer-grained adaptation?\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 4, "DATE": "2025-07-01"}