LLM-DAMVC: A Large Language Model Assisted Dynamic Agent for Multi-View Clustering
Authors: HaiMing Xu
Qianqian Wang
Venue: NeurIPS 2025 poster ()
Keywords: Multi-View Clustering
Primary area: deep_learning

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.

LLM-DAMVC: A Large Language Model Assisted Dynamic Agent for Multi-Viee Clustering
Haiming Xu!
Qianqian Wang"
'School of Telecommunications Engineering, Xidian University, Xi' Xi'an, China 240112110440stu xidian.ciicc edu. cn, ggwangexidian. edu. en
Abstract
Multi-view clustering integrates the consistency and complementarity of different views to achieve unsupervised data grouping. Existing multi-view clustering meth- ods primarily confront two challenges: i) they generally perform feature extraction in the feature domain, which is sensitive to noise and may neglect cluster-specift 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 dis- tribution and view quality. To address these issues, we propose a large language model assisted dynamic agent for multi-view clustering (LLMM-DAMVC, a novel framework that recasts multi-view clustering as dynamic decision- lem orchestrated by large language model. Specifically, each view is equipped with complementary agents dedicated to feature extraction. A dual-domain con- trastive module is introduced to optimize feature consistency and enhance cluster separability in both the feature domain and frequency domain. Additionally, LLM-assisted view fusion mechanism provides a flexible fusion weight learning strategy that can be adaptively applied to complex scenarios and significantly differ- ent views. Extensive experimental results validate the effectiveness and superiority of the proposed method.
prob-
Introduction
Nowadays, data from different sources modalities (i.e., multi-view data) have become increasingly ubiquitous, such as user profiles and behavioral data in social networks, multi-modal scanning results in medical imaging, and multi-variate sensor data generated by loT devices (12 3
5 6). These
multi-view data often contain richer and more comprehensive information than single-view data but also pose challenges such as heterogeneity, noise interference, and missing labels. Multi- Clustering (MVC), which aims to effectively partition unlabeled data by leveraging the consistent and complementary information across views, has become core technology in multi-view analysis, data mining, and pattern recognition 7 1O).
To effectively fuse multi-vien information, researchers have proposed various MVC methods. Early approaches primarily relied on non-negative matrix factorization, subspace learning, or spectral clustering (II: 12, [3, 9 14, (5). With the advancement of deep learning, deep MVC methods (e.g..
those based on autoencoders, graph neural networks, or contrastive learning) have made significant progress in representation learning and exploring nonlinear relationships (16) 17 18 19 20 21). Notably, some studies have attempted
address view fusion by using attention mechanisms to adaptively aggregate view features (22) 23). Additionally, to learn consistent representation with discriminative information, contrastive learning has been widely adopted to improve clustering performance by maximizing the similarity between positive samples and meanwhile minimizing that
*Corresponding Author
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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between negative samples (24, 25).. Despite their effectiveness, most of them focus on Teature-domail consistent representation learning. which neglects imperceptible and indistinguishable features in the original feature space. Nevertheless, these features can be separable easily in the frequency domain. Moreover, these methods typically rely on predefined rules to assign weights for each view, making them inflexible when applied in complex scenarios and processing heterogeneous views. Especially, their static strategies for determining view weights may lack adaptability adjusting strategies when facing drastic variations in view quality at the sample level. Existing methods generally lack centralized coordination mechanism capable of understanding view content, assessing view quality, and performing intelligent decision-makee -making.
To address these challenges and inspired by the powerful gpeision-makingg capacity of Large-scale Language Models (LLMs), we propose an LLM- ALM-Assitted Dynamic Agent for Multi- Clustering (LLM-DAMYC), Different from existing works that introduce L.LMs to MVC for feature extraction (26. LLM-DAMYC leverages the semantic reasoning and decision-making capabilities of LLMS for adaptive view fusion in MVC. This is achieved through synergistic architecture incorporating multiple agents per view and dual-domain contrastive learning mechanism. Specifically, each view is equipped with a standard agent and an adversarial agent, respectively focusing on semantic feature extraction and adversarial learning. A dual-domain contrastive learning module is introduced to optimize feature consistency in the feature domain and frequency domain. Finally, we utilize the information of the semantic feature quality, cluster structure quality, and intra-cluster compactness to construct a prompt for LLM to dynamically learn a fusion weight for each view. The main contributions of LLM-DAMCC are summarized as follows:
We propose novel MVC framework named LLM-DAMVC by introducing LLM as a decision-making module to evaluate the view quality in real time and dynamically assign aggregation weights, so that the model can adaptively adjust the fusion strategy according to the view characteristics and sample distribution.
We build a dual-domain contrastive learning module that integrates frequency-domaii contrastive learning with feature-domain contrastive learning. It maps the features to the frequency space through the FFT transform, capturing imperceptible and indistinguishable features in the original feature space and significantly enhancing the feature representation capability.
We conduct extensive experiments on several benchmark datasets and compare our method with ofte-of-thhean MVC methods. The experimental results and analyses demonstrate the effectiveness of the proposed method.
2 Related Work
2.1 Multi-view Clustering
Numerous MVC methods have been proposed the past few decades. For example, Large- scale Multi-View Spectral Clustering (MVSC) approximates each view's similarity graph via a bipartite an- chor graph, reducing time and space complexity of spectral clustering to near-linear while preserving cross-view manifold structure (27). Another line of work focuses adaptive fusion: Self-weighted Multi-view Clustering (SwMC) learns view-specific Laplacian graphs and their confidence weights jointly, enabling direct cluster assignment without extra k-means step (IO). To further account for sample-specific view importance, Localized multiple kernel m-mmans clustering extends kernel k-means multi-view data by learning sample-specific kernel weights through localized data fusion, which adaptively captures view importance and excels on biomedical datasets (28). Diversity-Inducee Multi- View Subspace Clustering (DiMSC) leverages the Hillbert-Schmie Independence Criterion to enforce statistical independence among view-specific representations and thus enhance comple- mentarity (13). However, these methods rely on shallow, linear representations and often fail to capture complex nonlinear dependencies across views, motivating the shift towards deep multi-view clustering techniques.
2.2 Deep Multi-view Clustering
Deep Multi Clustering (DMVC) leverages deep neural networks to learn non-linear representa- tions from multi- view data. Early approaches primarily focused on learning shared representation
2

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Multi-Agent Collaboration Module
Standard Agent
Image
X
Residual Goted Encoder
Advensarial Agent
LLM-Aasisted Dynamic Feature
Appregation Module
Dual-Domain Contrastive Learning Module
FFT
State confidince certainty
Policy Network
M
compactness
Frequency domain contrastive learning
contrastive learning
quality
Standard Agent
Audio
X
dpp
Residual Gated
Encoder
Adversarial Agent
Multi-Agent Collaboration Module
Figure 1: overall architecture are processed by a Residual Gated Encoder to generate shared latent features (H"). These features are then into two parallel branches, a Standard Agent and an Adversarial Agent, to produce initial predictions (P P and contrastive embeddings (Zus, Z.de). The encoder is subsequently refined Dual-Domain Contrastive Learning Module, which enforces consistency domain (via FFT). Concurrently, a state assessment based on multi-faceted quality indicators (q informs an LLM and a parallel heuristic policy to generate dynamic aggregation weights (w.). Finally, these weights guide the fusion of all agent predictions into a unified probability distribution (P), from which the final cluster assignments are derived.
proposed LLM-DAMVC framework. Multi view data (X*
these embeddings both the feature domain and frequency
space, often employing autoencoders for cross-view reconstruction (29) or deep canonical correlation analysis (DCCA) maximize inter-view correlations (30: 31. Subsequent advances explored more sophisticated architectures. Graph-base based methods, for instance, utilize Graph Neural Networks (GNNs) capture the underlying topological structure of the data [6 32). More recently, to address the challenge of static view fusion, attention mechanisms have been widely adopted to learn dy- namic, instance-specific weights for view aggregation (33) (22). However, while these attention- based methods offer improved adaptability, they typically rely predefined rules to assign weights for each view, making them inflexible when applied in complex scenarios and processing heterogeneous views.
3 METHODOLOGY
3.1 Notations and Problem Definition
Multi-view clustering aims to leverage complementary information from different feature spaces to achieve better clustering performance. Given multi-view dataset X' {X Xx2 .x....,x X where X N represents the feature matrix of the w-th view containing N samples with d, dy-dimensional features. The goal multi-view clustering to partition these N samples into K semantically consistent clusters {C1, C2..... Ck) where K represents the number of inherent classes the dataset.
In this section, we introduce novel deep multi-view clustering method called LLM-assisted Dynamic Agent Multi- View Clustering (LLM-DAMVC), which reformulates MVC as a dynamic decision- making problem. Our approach introduces specialized agents that process individual views and dynamically adjust their contributions based on their performance. Instead of using a predefined fusion strategy, ILM-DAMVC employs dynamic routing mechanism that determines how to optimally combine these features based on their quality and complementarity. I.LM-DMMVC mainly consists of three key modules: Multi-Agent Collaboration Module,
Contrastive

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Learning Module, and ALM--Assistt Dynamic Feature Aggregation Module. Specific descriptions of these modules will be provided in the subsequent sections.
3.2 Mueti-Agent Collaboration Module
The Multi-Agent Collaboration Module serves the fundamental feature extraction component of our LLM-DAMVC framework. This module is composed of Residual Gated Encoder, Standard Agent, and Adversarial Agent. The Residual Gated Encoder transforms the input into a latent representation H The two specialized agents operate on the latent representation H" in parallel for distinct and complementary purposes.
Given the input data matrix X R.dx for view u, we first transform it into view-specific latent representation through a residual gated encoder Eg, For the first L 1 layers, each composed of linear layer, a batch normalization (BN) layer, and an activation layer. Starting with the initial input Hj = X", each intermediate layer transforms the features as:
Residual Gated Encoder:
H ReLU (BN(H)-1 Wi bi)), 1= ...,, 1,,,,
where R.LU(.) denotes the ReLU activation function; BN(.) is batch normalization, and W., b learnable parameters. The final layer (L-th layer) incorporates self-gating mechanism and residual connection to enhance representational capacity 34 35):
H [1 + (H (gmne) @ - +af(x*)
+
(2)
where H" E RR-xxd xd represents the latent embedding, sigmoid(.) is the sigmoid function, W
gate is
the gating parameter, @ denotes clement-wise multiplication, and > 0 is a scalar hyperparameter controlling the strength of the residual connection. Crucially, f() a linear projection ensure dimensional consistency for the residual connection.
Standard Agent: We first incorporate standard agent to extract stable features and learn cluster structure, which transforms the latent representation into a clustering probability distribution and standard embedding. To be specifie, we utilize learnable mapping Qad composed of two linear layers, a BN layer, a ReLU activation layer, and a softmax operation to learn the clustering probability distribution pd NxNK from H" as follows:
P srd Aad H
Then we employ another learnable mapping End composed of two linear layers, BN layer, and ReLU activation layer, to project H into a standard embedding Z NR.xxd:;
Znd Exid (H))
Adversarial Agent: Operating in parallel, introduce adversary agent that explores the bound- aries of the feature space to enhance robustness and discriminability. It first learns the adversary clustering probability distribution Pady through daav that has the same structure Qad:
P adv Patv (H") (H")
To obtain the adversarial embedding 2 Nxd, Exid:
xd= we use mapping &ndy with similar structure to
Z Eady (H")
(6)
Then, we compute 3" = arg max (P as pseudo-labels, which guides the generation adversarial samples X by computing input gradients:
x
X
+ 8. sign (Vx- CE(P: advi 8n))
where V represents gradient operation, CE denotes the cross-entropy operation, sign is the sign function, and 8 > 0 controls the perturbation intensity. Then, X is used to retrain the adversarial agent, enforcing robustness against input perturbations, and is not propagated subsequent modules. Subsequently, we build discriminator vé with MLP to distinguish real features (H" (X")) and

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generated features -H EX")),, This adversarial branch introduces min-max game between the encoder and the discriminator to enhance representation robustness with discriminator loss dise
Ddisc - Exx [log( 1/ (H")) 1xxx [log(1 - (*())
(8)
We compute discrimination scores by s = ( (s E R To enforce cross-vicr view alignment and high-quality clustering, add an alignment loss follows:
Lalign Σ E A(H H + V(V -1 u=l use
1
Σ
E
DRL.
(P
P
where A(),)) maximizes the canonical correlation between view-specific latent features H by operating on their cross-covariance matrix, and DRI. KL-divergence to ensure cluster distribution consistency.
3.3 Dual-Domain Contrastive Learning Module
To improve the discriminativeness and consistency, we propose Dual-domain Contrastive Learning module that integrates analyses of the feature domain and the frequency domain. This module uses the standard embedding Baa and the adversary embedding Z to build contrastive loss in the two domains. For easier presentation, we employ z to represent the embedding of i-th sample in view from Z Zuiv-
Feature-domain Contrastive Learning: For corresponding samples across different views v and u. we compute their cosine similarity:
(z |z
z
P,0 by
(10)
|z)
Following the contrastive learning framework (6),, we consider features from different views of the same sample i as positive samples and different sample j (j F i) as negative samples. Then, we construct the contrastive loss as follows:
1
Σ
Σ log
exp(s P.u /T)
Cieature
(11)
N
Σy=1 exp(s 2.3 /T)
ISeVV ISISN
where + > 0 is a temperature hyperparameter. This objective promotes alignment of corresponding cross-view samples while enforcing separation from non-corresponding instances.
Frequency-domain Contrastive Learning: We extend contrastive learning to the frequency domain capture global structural patterns that remain clusive in the feature space. For each feature vector feature z E Rd, we apply the Fast Fourier Transform (FFT) to compute the frequency-doman 2.' = F(z,), where FF denotes the FFT operation and 2 E C the complex-valus frequency spectrum. We extract the amplitude spectrum |ž,| E Rds, which characterizes the energy distribution across frequency components while maintaining translation invariance.
For the embedding z of an anchor sample from view v, we construct a contrastive triplet (z", z,, z by selecting corresponding embedding z in another view u (u F v) as positive sample and select another sample z (j * i) negative sample. For efficient computation, we only sample M negative samples, and the frequency-domat contrastive loss is defined as follows:
||
12
Σ E
Lrrequency
(12)
NMV
2
+6
Isu ISISN (EJ(K)
where >> is small constant for numerical stability, I I(i) represents the set indices for negative samples corresponding to anchor i.
We obtain the dual-domain contrastive loss with a balancing factor p as follows:
Lunat Lienture + pLrrequencys
(13)
The dual-domain contrastive learning enhances the features and helps to capture both local discrimi- native details and global structural patterns.
5

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3.4 LLM-Assisted Dynamic Feature Aggregation Module
To address the limitation of static fusion strategies traditional MVC, we propose the LLM-Assisted Dynamic Feature Aggregation Module, which leverages an LLM to dynamically assess and aggregate
multi-view representations. This module processes the latent features (H") J== clustering predictions
(P sdd P adv ) and discriminative score (s J from the preceding stages.
For agent of the e-th view with type (a (adv, std)), compute quality indicator vector L E R44X to provide a multi-faceted assessment of its real-time performance:
[[[maxx [ (1, [))
C(H", P
9
(14)
1 H(P
(i)
where [[mma(PP((t.))) measure the agent's 's average prediction confidence, ((") measures cluster structure quality, Ç calculates compactness score based on intra-cluster distances, 1 H(PP) measures prediction certainty, H is entropy operation, and IE] (s (i)) measures the average representation quality score, respectively.
The LLM-assisted mechanism processes these quality indicators q. to generate dynamic aggregation strategy. Specifically, the quality vectors are formatted into a structured prompt and fed to frozen large language model (LLM). The LLM analyzes the global performance of all agents and outputs high-level routing policy, the core component which is a confidence threshold Te for filtering out unreliable agents. Based this policy, compute adaptive aggregation weights for each agent. The raw weight r. for each agent is determined by:
(P
|H"]| if E|max(PP (ú, :1)] 2 To
r"
(15)
0.
otherwise
where| -|- I| denotes the Frobenius norm. The raw weights are then normalized to sum one via softmax, yielding the final weight {w"} as follows:
exp(r")
(16)
EV Ene (Midady) exp(r"))
The final aggregated prediction obtained by weighted fusion of all agent predictions:
"PP E NRK
(17)
oE (std.ady)
The clustering loss is formulated as:
K
K
N
Letus
Uik log Pik
+ A1
P
+ A2
log max (P:))
(18)
TE.Tighh 1m
where Pa is the (i, k)-th entry of the aggregated prediction P; Inigh is the set of high-confidence samples, and sample i considered as high-confidence sample max(P:) exceeds predefined threshold; y, is one-hot vector indicating the assignment of sample i E Thigh and Yı = for Ind(max (P.)) and 0 otherwise; Ind(.) the indexing operation. The second term encourages balanced cluster sizes by minimizing the L1 deviation of the mean assignment P; N Σ, Ps from the uniform distribution 1/K, and the third term promotes prediction clarity.
3.5
The Objective Function
The total loss function combines the losses above and is as follows:
Lioaal = Ldiee + HCatign + XLcomt + BL
(19)
where 7, A, and are hyperparameters balancing different parts of the objective.
The final cluster assignments are directly obtained from the aggregated prediction of the LLM- Assisted Dynamic Feature Aggregation:
arg (P for 1 S iis N,
(20)
where q 21, ... K} denotes the cluster index for the i-th sample.

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EXPERIMENT
4.1 Datasets Metric
We evaluate our method on six benchmark multi-view datasets to validate its effectiveness across diverse data types: SUS-WIDE (NUS) (37), an image dataset containing 6,251 samples described by 5 visual feature views; BDGP (38), a Drosophila image dataset with 2,500 samples and views (visual and textual); Handwritten (39), digit recognition dataset comprising 2,000 samples represented by 6 heterogeneous feature views; MNIST-USPS (38), a cross-domain digit benchmark with 5,000 samples from two complementary image datasets; Reuters (40), a multilingual news corpus consisting of 1,800 short articles and their associated topics, represented by 5 language-specific views; and CCV (40), a consumer video dataset with 6,773 samples and 3 deep feature views. We report three standard clustering metries: Accuracy (ACC), Normalized Mutual Information (NMI), and Purity (PUR). All experiments are implemented in PyTorch and conducted on an NVIDIA GeForce RTX 4090 GPU.
4.2 Comparing Methods
To evaluate LLM-DAMVC on complete multi-view datasets, we benchmark against state-of- the-art multi-view clustering (MVC) methods, spanning classical baselines, deep representation learning approaches, contrastive learning techniques, and advanced dynamic fusion strategies. As classical baseline, K-Means (4I minimizes intra-cluster distances for partitioning but overlooks multi-view complementarity. Deep MVC methods include DCP (42), EE-IMVC (43), ASR (44), and MFLVC (24). Contrastive approaches include CVCL (8) and COMPLETER (25). Advanced techniques include ADMC (45), which enhances efficiency with active sample selection in semi- supervised settings, and COPER (40), which aligns cluster assignments through correlation-based permutations.
Table I: Clustering performance comparisons on selected datasets. Best results blue bold, second best red bold.
MNIST-USPS
Methed
NMI
PUR
ACC
NMI
NMI
PUR
ACC
XMI
PUR
K-Means E11
20.90
27.45
05.20
18.95
37.21
39:45
49.30
45.40
6.70
5.90
8.97
EE-IMVC +5
22.29
10.35
30.02
58.00
71.76
87.76
59.30
$1.07
89.30
76.00
08.04
76.45
19.05
11.39
16.38
23.37
15.22
36.46
ASR [4]
21.50
13.73
38.97
97.68
92.63
97.65
93.95
88.26
93.95
97.90
94.72
7.90
15.51
18.42
14.55
24.06
17.41
22.73
DSIMIV 4G
25,89
18.86
43.27
99.34
96.86
99.04
$7.20
S1.39
87.20
99.34
98.13
99.34
24.35
22.17
20.41
31.90
30.70
30.54
DCP 42
19.43
5.45
30.72
97.04
92.43
97.04
55.75
35.05
83.75
99.02
97.29
99.02
26.66
32.74
21.04
20.04
15.61
14.22
MELXC 23
24.11
16.43
30.87
98.72
96.13
98.72
56.35
35.95
86.55
99.06
99.01
99.66
21.14
19.95
20.37
31.25
31.69
33.90
CVCL. [8]
25.65
13.06
30
99.20
97.29
09.20
97.38
94.05
07.35
99.70
99.13
99.70
55.64
31.14
57.35
26.25
26.25
21.17
COMPLETER
22.48
7.71
38.73
90.05
56.18
59.95
69. 36
73.03
69:70
39.08
$8.85
89.02
25.65
31.73
25.34
26.58
22.51
21.14
ADMC
24.36
15:97
40.06
86.96
96.12
96.96
64.11 10
81.17
84.10
91.26
95.50
91.26
77.33
70.49
77.33
23.25
20.68
23.57
COPER
25:55
11.67
35.54
39.65
73.92
75.00 96
57.55
77.15
89.05
99.58
99.64
99.
53.15
31.10
51.79
28.06
26.32
24.57
Our Method
37.11
37.51
48.53
99.67
98.76
99.64
98.66
95.36
98.43
99.92
99.63
99.92
78.76
76.48
79.33
46.55
55.77
49.51
4.3 Experimental Analysis
Performance Comparison: To comprehensively evaluate the efficacy of our proposed method, we conduct an extensive comparative analysis against multi-view clustering methodologies across six challenging benchmark datasets. As detailed in Table [ the empirical results unequivocally demonstrate the superior performance of our approach. Our method, LLM-DAMVC. consistently outperforms all baseline models across all datasets and evaluation metrics, which underscores its exceptional generalizability and robustness to diverse data characteristics. The superiority of our method is particularly illustrated on well- structured datasets such BDGP and MNIST-USPS, where it achieves superior clustering outcomes with both accuracy and purity levels surpassing 99%. On these datasets, our approach markedly surpasses even the most competitive baselines, showcasing its capability to discern fine-grained cluster structures. Furthermore, when confronted with more intricate and noisy datasets like CCV and Reuters, our method sustains high efficacy. This consistent performance across varied data modalities validates the effectiveness of our LLM--sssittd dynamic fusion mechanism in adaptively handling complex, real-world data distributions.
We analyze the sensitivity of our model to the hyperparameters X, B, and y that weight the contrastive loss (Luom), clustering loss (Letus), and alignment loss (Lalign) The model is robust to the choice of A: performance remains stable over wide range of values, which
Hyper-pane parrem-prrameter Analysis:
7

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100
100
100
ACC (%)
ACC (%)
ACC (%)
50
50
0.01
0.01
0.01
0.1
o0
0.1
10
100
100 1007
1000
1.0
0.1
10
0.1
(a) Varying À and 8 (fixed 1.0)
(b) Varying y and B (fixed 1.0)
(c) Varying X and y (fixed / 1.0)
Figure 2: Parameter sensitivity analysis of our method on the MNIST-USPS dataset. We investigate the impact of hyperparameters X, 8, and 7y on clustering accuracy (ACC). Each subplot shows the performance landscape by varying two parameters while keeping the third fixed at its optimal value (e.g.. 1.0).
shows that the contrastive loss consistently fulfills its role of pulling similar samples together and pushing dissimilar ones apart, and this function is reliably effective without requiring precise tuning. In contrast, for the parameters 7 and B, there exist performance peaks only at specific values and drops when deviating, demonstrating that the alignment loss and clustering loss must be carefully balanced to work properly. The alignment loss is responsible for establishing cross-view consistency by aligning representations from different views into a shared space, and the clustering loss directly optimizes the cluster structure by encouraging samples to form coherent groups. By comprehensively analyzing the three figures, we found that high performance achieved when all three losses are present. Removing or severely weakening any one of them leads to degradation, which confirms that each loss performs distinct and necessary function: contrastive learning handles fine- fineggrineed sample discrimination, alignment ensures cross-view agreement, and clustering enforces global semantic grouping.
Table 2: Progressive ablation study showing the contribution of each component in I.M.-DAMVC. Best results in blue bold, second best in red bold.
Method
NUS
HDGP
Handwrilten
MINIT-USPS
Renters
CCV
ACC
NMI
PUR
ACC
NMI
PUR
ACC
NMI
PUR
ACC
NMI
PUR
ACC
NMI
PUR
ACC
XMI
PUK
Busdins (Quly Causee)
23.70
Caas
24.50
25.80
87.75
90.30
87.75
97.30
97.73
65.48
73.20
Exte (Full)
98.66
95.36
95.43
99.92
99.63
99.92
78.76
76.48
79.83
46.55
To rigorously evaluate the contribution of each proposed component, con- duct a progressive ablation study. with detailed results presented in Table 2 The findings clearly demonstrate that each module synergistically contributes to the final performance LLM-DAMVC. Our analysis begins with bascline model trained solely on the fcature-domale contrastive loss (Lieature), which establishes foundational performance level. From this baseline, we observe a clear and consistent trend of improvement as each new component is incrementally introduced. Notably, the most significant performance increase is observed upon the integration of the frequency-domain contrastive loss (+ Lirequency) and the clustering loss (+ Lotus). The former's 's impact strongly validates analysis is crucial for capturing global
Ablation Studies:
our core hypothesis that incorporating frequency-dome structural patterns that are often missed by conventional feature-domat substantial gains from the latter highlight the undeniable necessity of a direct and well-structured clustering objective guide the model towards forming high-quality and coherent cluster structures. Finally, the inclusion of the discriminator loss (+ Laise) and the alignment loss (+ Laliga) provides further valuable refinements, incrementally improving the model's 's capabilities to its best performance. The full model, incorporating all components, consistently achieves the best results across all datasets.
methods. Meanwhile, the
Analysis: Figure 3 iillustrates feature representation evolution on BDGP dataset, from unstructured raw features (Figure 3a) to well-defined clustering structures, validating our LLM- DAMVC framework. Figure (left) shows convergence trajectories stabilizing after 60 iterations with monotonic loss descent, demonstrating robust convergence. Figure (right) reveals DeepSeek- 8B outperforming across all datasets, particularly on complex ones (NUS, CCV), while even the 1.5B
8

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Raw Features
(b) Epoch=10
Epoch=50
(d) Epach=100
Figure 3: T-SNE visualization on the BDGP dataset with increasing training iteration.
Model
Performance (%6)
Deepdeel--88
-00
80
DeepSeek-78
DeepSeak-1.59
Metrics
60-
NMI (%)
60
40-
Loss
20
20
-10
MUS
cCV
Reuters
MNST
BDGP
Hand
20
50
60
80
90
Iterations
(a) Convergence curve
(b) Performance with different LLMs
Figure 4: Convergence curve and performance different LLM decision models
variant maintains acceptable performance structured datasets (MNIST, BDGP) These findings highlight both LLMs' critical role in multi-view clustering and our framework's 's scalability under diverse computational constraints.
4.4 Limitations
Despite achievements, the designed L.MM-Assisted Dynamic Feature Aggregation Module adopts general LLMS to build the decision-making module for view fusion. Though it indicates effectiveness, the performance could be improved by adjusting the LLMs be dedicated view fusion tasks in MVC. Therefore, our future work will fine-tune the LLMs to make them more suitable for MVC. Besides, we will also consider introducing the model distillation technique to enhance the efficiency of the dynamic feature aggregation process.
5 Conclusions
In this paper, we propose LLM-DAMVC, a novel multi-view clustering framework that reformulates view fusion dynamic decision-making process orchestrated LLMs. To address the limitations of existing methods, i.e., their reliance on feature-domain representation learning and static fusion strategies, introduce key modules. First, dual-domain contrastive learning module jointly optimizes feature consistency and cluster separability in both the original feature domain and the frequency domain via FFT. thereby capturing subtle structural patterns that are indistinguishable in the raw feature space. Second, an LLM-assisted dynamic fusion mechanism leverages semantic reasoning evaluate view quality at the sample level based on semantic feature quality, cluster structure coherence, and intra- citra-cluster compactness and adaptively assigns fusion weights without predefined rules. Unlike prior works that employ LLMs for feature extraction, our framework uniquely positions the LLM as decision- decision-making module that coordinates multi-view integration in response to data distribution shifts and view heterogeneity. Extensive experiments across six benchmark datasets demonstrate that LLM-DAMVC consistently outperforms tate-of-the-arr methods, particularly in complex scenarios with heterogeneous views and varying data quality, validating the effectiveness of dynamic LLM-assisted clustering method.

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Acknowledgments
This work is supported by the National Natural Science Foundation of China under Grant 62176203, the Fundamental Research Funds for the Central Universities (ZYTS25267, QTZX25004), and the Science and Technology Project of Xi'an (Grant 2022JH-JSYF-0009, Open Project of Anhui Provin- cial Key Laboratory of Multimodal Cognitive Computation, Anhui University (No. MMC202416), Selected Support Project for Scientific and Technological Activities Returned Overseas Chinese Scholars in Shaanxi Province 2023-02,
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Reviews:
[{"review_id": "l2OCXHVW58", "reviewer": "Reviewer_hDjh", "summary": "This 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.", "questions": "Q1. 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?", "limitations": "Yes.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 4, "strengths_and_weaknesses": "Strengths:\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.", "was_revised": false, "final_justification": "", "rebuttal": "**Q1: Regarding the novelty of applying LLMs to clustering, and whether the LLM requires training or fine-tuning.**\n\n**A1:** Unlike prior methods using LLMs for static feature extraction, the novelty of our work lie in that we leverage its advanced reasoning to interpret complex inter-view relationships and adapt fusion strategies, without any LLM training or fine-tuning. This core mechanism, combined with our collaborative dual agent architecture and innovative dual domain (spatial+frequency) contrastive learning, is used for multi view clustering.\n\n---\n\n**Q2: Regarding the analysis of how different LLMs affect performance.**\n\n**A2:** We test the clustering performace with different LLMs (i.e., DeepSeek-R1(8B) and LLAMA3), as summarized below:\n\n| Dataset | DeepSeek-R1(8B)         | LLAMA3(8B)              |\n| ------- | ----------------------- | ----------------------- |\n| NUS     | 37.11 \\| 37.51 \\| 48.53 | 31.37 \\| 29.91 \\| 42.14 |\n| BDGP    | 99.67 \\| 98.76 \\| 99.64 | 99.02 \\| 97.66 \\| 99.02 |\n| HW      | 98.66 \\| 95.36 \\| 99.92 | 85.97 \\| 82.21 \\| 79.87 |\n| MNIST   | 99.92 \\| 99.63 \\| 99.92 | 92.91 \\| 93.83 \\| 92.91 |\n| REUTERS | 78.76 \\| 76.48 \\| 79.83 | 79.83 \\| 75.97 \\| 80.17 |\n| CCV     | 46.55 \\| 55.77 \\| 49.81 | 42.98 \\| 48.35 \\| 41.97 |\n\n---\n\n**Q3: Does the \"view quality\" evaluation capture view complementarity and noise levels, beyond just prediction certainty?**\n\n**A3:**  Our view quality assessment metric focuses on evaluating the downstream impact of view features on the final clustering task, rather than directly quantifying noise or complementarity. Although these two metrics cannot be directly measured, indirect inference remains feasible. For instance, a view severely corrupted by noise struggles to generate clear and compact feature representations, resulting in poorly organized, more dispersed clustering structures with larger intra-cluster distances and consequently lower feature quality scores. This metric indirectly quantifies the harm of noise to the clustering task. Regarding complementarity, highly complementary views facilitate the model in forming clearer and more separable clusters by providing unique discriminative information, corresponding to higher feature quality scores, whereas redundant views yield lower scores.Crucially, this metric is not used in isolation but as part of a holistic system-level assessment by our LLM-guided router. The LLM integrates the metric with other indicators (e.g., prediction confidence, entropy, resource usage) to perform context-aware reasoning. For example, it can recognize that a view with an average performance score but persistent divergence from high-confidence consensus views may provide critical decisive resolving information, thus retaining it to address ambiguous samples. This cooperation between the metric and LLM reasoning is a core strength of our framework.   \n\n---\n\n**Q4: Why not call the LLM more frequently to capture finer, dynamic changes in the data?**\n\n**A4:**  We employ the LLM as a strategic planner that sets the overarching fusion policy based on a stable and holistic view of the data, which does not require batch-level frequency. Frequent LLM calls would introduce prohibitive computational latency and risk destabilizing the training process by overreacting to batch-level noise. To capture finer dynamics without these drawbacks, we employ a lightweight policy network that updates every batch. This creates a highly effective \"LLM macro-guidance + network micro-adjustment\" framework, where the LLM provides robust strategy while the policy network handles rapid, tactical adaptations, thus achieving both clustering performance and computational efficiency.\n\n---\n\n**Q5: A detailed introduction should be given on the application of LLM in clustering, and some formatting errors should be corrected to improve overall clarity.**\n\n**A5:**  We will thoroughly revise the manuscript and expand our work on the latest applications of LLM in clustering tasks. In addition, we will carefully proofread the entire paper and correct all formatting and formatting errors."}, {"review_id": "qv9G1FVWdD", "reviewer": "Reviewer_sGij", "summary": "This 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.", "questions": "1. 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?", "limitations": "Yes.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "- 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.", "was_revised": true, "final_justification": "The authors' response has addressed my concerns. I would like to keep my original score.", "rebuttal": "**Q1: What is the unique advantages of using an LLM for dynamic weight assignment compared to traditional attention mechanisms or the lightweight policy network mentioned in the paper?** \n\n**A1:** The unique advantage of the LLM lies in its capacity for semantic reasoning and structured decision-making, which transcends the direct input-to-weight mapping learned by conventional attention or policy networks. As formulated in Equations (18) and (19), the LLM does not just learn a function; it interprets a structured prompt containing semantically meaningful metrics (e.g., prediction confidence, cluster quality). This allows it to perform higher-order reasoning, such as identifying a view as \"high-confidence but low-certainty\" and dynamically adjusting the fusion strategy accordingly—a capability that simple neural networks struggle to generalize, especially on out-of-distribution or complex view quality combinations. While the policy network provides efficient, fine-grained adjustments, the LLM provides a robust, logic-driven macro-policy. Our ablation study quantitatively confirms that removing this reasoning component leads to a catastrophic performance drop, demonstrating LLM's important contribution.\n\n---\n\n**Q2: How does frequency-domain analysis help to improve performance for generic high-dimensional feature vectors that are not from signals or images?**\n\n**A2:** For any feature vector, the Fast Fourier Transform (FFT) provides a principled decomposition into components of varying \"frequencies,\" where we conceptualize low-frequency components as representing the global, slowly-varying structure of the feature manifold, and high-frequency components as capturing local, fine-grained details and noise. Our Frequency-Spectral Contrastive Learning (Eq. 13-15) operates on the magnitude spectrum of these features, which is invariant to translation (i.e., shifts in the feature vector). By encouraging the low-frequency spectra of corresponding samples from different views to be similar, our model learns to align their global structural patterns, effectively acting as a powerful structural regularizer. This forces the model to capture consistent high-level semantics across views while simultaneously reducing sensitivity to high-frequency noise or minor feature perturbations."}, {"review_id": "45NeB3go1k", "reviewer": "Reviewer_MLHN", "summary": "The 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.", "questions": "(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?", "limitations": "Yes", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 4, "originality": 3, "strengths_and_weaknesses": "Strengths 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.", "was_revised": true, "final_justification": "Most of my concerns have been addressed, I will maintain my positive score.", "rebuttal": "**Q1: How does your framework mitigate the negative impact of potentially unreliable pseudo-labels generated by the adversarial agent, especially in the early stages of training?** \n\n**A1:** We adopt two strategies to address this issue. First, at the loss-function level, we generate pseudo-labels based on cluster centroid proximity, not direct agent output. We then apply a confidence-based filter (high_conf_mask) to select only the most reliable labels for supervision. The influence of this supervision is further controlled via a curriculum learning scheme, where its loss weight (epoch_factor) gradually increases as training stabilizes. Second, our LLM-Assisted Dynamic Feature Aggregation (LDFA) module provides structural-level control. For Equations (17) and (19), the LLM assesses each agent's output quality and assigns dynamic weights. An adversarial agent producing unreliable results receives a lower weight, thus its negative impact on the final fused representation is actively suppressed.\n\n---\n\n**Q2: Have you considered if applying established clustering algorithms like K-Means on the final embedding$ \\mathcal{Z} $ would yield better results than the current direct assignment method?** \n\n**A2:** The entire learning objective, particularly the clustering-specific loss $L_{clus}$ (Eq. 23), is explicitly formulated to train the network to produce a linearly separable embedding space $\\mathcal{Z}$. The model is not merely encouraged to group similar samples, but to directly map their final representations to high-confidence categorical distributions. Applying K-Means would introduce a distance-based clustering objective (Euclidean distance minimization) that is not necessarily aligned with the primary, clustering-oriented loss functions used for representation learning. This objective mismatch could lead to suboptimal results by re-interpreting a space that was already optimized for direct assignment. Furthermore, our approach ensures stability of clustering result by avoiding the initialization sensitivity and additional hyperparameters inherent to iterative algorithms like K-Means.\n\n---\n\n**Q3: Could you clarify the specific justification and significance of using heterogeneous (standard and adversarial) agents, as opposed to a single agent design?** \n\n**A3:** Using heterogeneous agents aims to simultaneously optimize for both stability and discriminability. The Standard Agent is dedicated to learning the stable, core structure of clusters via contrastive learning. Concurrently, the Adversarial Agent acts as a targeted regularizer, exploring the decision boundaries by generating adversarial samples (Eq. 8) to enhance inter-cluster separability and robustness. A single agent would struggle to balance these conflicting objectives. The significance of this dual-agent design is that it yields a final representation that is both structurally coherent and highly discriminative."}, {"review_id": "YrnHAAXXaE", "reviewer": "Reviewer_yCME", "summary": "this 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).", "questions": "Please refer to \"Summary\"", "limitations": "Please refer to \"Summary\"", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Please refer to \"Summary\"", "was_revised": true, "final_justification": "My concerns have been addressed, and the clarifications provided helped me better understand the contribution of this work. Therefore, I have decided to raise my overall score.", "rebuttal": "**Q1: The motivation appears to overemphasize feature extraction, while the primary objective of Multi-view Clustering (MVC) is arguably the exploitation of cross-view information and correlations. A clarification on how the proposed method balances these two aspects is needed.** \n\n**A1:** Our framework is predicated on the principle that robust feature representation is not a substitute for effective cross-view correlations, but rather a prerequisite for enabling such correlations. We acknowledge that the core of MVC lies in leveraging multi-view information and their inherent correlations. However, without high-quality features, even extensive utilization of multi-view information would yield suboptimal outcomes. To strike this balance, we employ a multi-level optimization strategy. At the foundational level, our model aligns views by minimizing cross-view contrastive loss, ensuring semantic consistency at the sample level. To capture deeper structural similarities, we introduce Frequency-Domain Contrastive Regularization , which aligns the global spectral patterns of features, thereby imposing a more profound form of consistency regularization. Finally, at the decision-making layer, a consistency loss based on KL divergence ensures that the final cluster assignments are consistent across all views. Thus, our approach does not merely focus on feature extraction; instead, it establishes a collaborative hierarchy that fully leverages multi-view information across diverse dimensions (i.e., feature space, frequency domain, and decision space). Therefore, we do not over-rely on feature extraction but rather “treat high-quality features as the cornerstone for multi-dimensional and in-depth cross-view information fusion.\n\n\n\n---\n\n**Q2: Given that dynamic fusion strategies have been explored in prior MVC research, what are the unique contributions and specific novelties of the proposed dynamic fusion mechanism compared to existing approaches?** \n\n**A2:** While existing schemes explore dynamic fusion, most rely on single metrics for “dynamic weighting” or even adaptive weighting, which fundamentally differs from our approach. Our aggregator leverages a self-attention mechanism to enable context-aware, mutually enhancing feature interactions prior to aggregation. Critically, we pioneer introducing LLMs into the decision loop: rather than performing numerical computations, LLMs execute human-like, logic-based strategic reasoning. This post-LLM integration conducts multi-dimensional state assessment—moving beyond single metrics to holistically evaluate complex states encompassing prediction quality, cluster structure, and resource efficiency. This strategy indicate more flexible capacity when processing complex scenarios (e.g., “Agent A has high confidence but poor cluster balance, while Agent B shows the opposite”) and generate complete strategies featuring primary/backup agents, confidence thresholds, and fusion modes. In summary, compared with existing dynamic fusion strategies, our method is a high-level semantic reasoning-guided fusion strategy.\n\n---\n\n**Q3: Could a precise, operational definition of \"view-quality\" be provided? Furthermore, what concrete experimental evidence can be offered to substantiate the central claim that static fusion strategies are inadequate for handling sample-level variations in view quality?** \n\n**A3:** We consider view quality is not a global attribute but a dynamic, sample-specific, and context-dependent state. Therefore, view quality should also be discussed at the sample level rather than solely at the global view level. Our framework operationalizes this insight by forgoing predefined criteria and instead implementing a real-time, multi-dimensional state assessment mechanism: as detailed in our paper, this mechanism evaluates a metric vector for each view on individual samples, encompassing confidence, entropy, cluster balance, feature quality (computed via intra-class distances), resource efficiency (integrating computation time and memory usage), and consistency with historical predictions—making such multi-dimensional evaluation far more comprehensive than static definitions.\n\nTo empirically substantiate our claim regarding the inadequacy of static fusion, we conducted a new \"View-Quality Perturbation Experiment\" on the CCV dataset. We partitioned the data into two class-based subsets (Group A: classes 0-9; Group B: classes 10-19) and selectively introduced significant noise to View 1 for Group A and View 2 for Group B, simulating drastic sample-level quality variations.\n\n| Method                                              | ACC   | NMI   | PUR   |\n| --------------------------------------------------- | ----- | ----- | ----- |\n| Static Fusion (Fixed, Globally-Optimized Weights)   | 21.05 | 24.31 | 23.40 |\n| Semi-Static Fusion (Batch-Level Adaptive Weights)   | 28.71 | 31.54 | 30.22 |\n| Our Dynamic Fusion (Sample-Level LLM-Guided Policy) | 42.92 | 52.06 | 46.20 |\n\n---\n\n**Q4: The experimental evaluation is not comprehensive enough in two aspects: insufficient description and small scale of the dataset, and the lack of key SOTA baseline methods.** \n\n**A4:** Regarding Dataset Description and Scale,Detailed statistics are provided below. While our selection includes standard benchmarks, we contend that characterizing them as uniformly \"small-scale\" overlooks their strategic diversity and complexity. Datasets such as NUS-WIDE-10, CCV, and Handwritten are recognized medium-scale benchmarks posing significant challenges due to their high dimensionality, view heterogeneity, and number of clusters.\n\n| Dataset     | Views | Samples | Classes |\n| ----------- | ----- | ------- | ------- |\n| BDGP        | 2     | 2,500   | 5       |\n| MNIST-USPS  | 2     | 5,000   | 10      |\n| Handwritten | 6     | 5,000   | 10      |\n| NUS-WIDE-10 | 5     | 6,251   | 10      |\n| CCV         | 3     | 6,773   | 20      |\n| Reuters     | 5     | 600     | 6       |\n\nWe conducted the following comparative experiments using the relevant methods mentioned by the reviewer, including comparisons with t-SVD-MSC and TL-MSC schemes. The results are as follows:\n\n| Dataset         | Method       | ACC       | NMI       | Purity    |\n| --------------- | ------------ | --------- | --------- | --------- |\n| **REUTERS**     | T-SVD-MSC[1] | 27.50     | 10.97     | 28.00     |\n|                 | MVSC-TLRR[2] | 21.09     | 11.72     | 21.61     |\n|                 | **Our**      | **78.76** | **76.48** | **79.83** |\n| **HANDWRITTEN** | T-SVD-MSC[1] | 95.75     | 91.86     | 95.75     |\n|                 | MVSC-TLRR[2] | 81.51     | 79.12     | 81.51     |\n|                 | **Our**      | **98.66** | **95.36** | **98.43** |\n| **BDGP**        | T-SVD-MSC[1] | 93.96     | 83.56     | 93.96     |\n|                 | MVSC-TLRR[2] | 95.32     | 88.61     | 95.32     |\n|                 | **Our**      | **99.87** | **98.76** | **99.64** |\n| **MNITS-USPS**  | T-SVD-MSC[1] | 62.66     | 59.86     | 64.44     |\n|                 | MVSC-TLRR[2] | 63.72     | 58.52     | 67.85     |\n|                 | **Our**      | **99.92** | **99.63** | **99.92** |\n\n[1]Xie Y, Tao D, Zhang W, et al. On unifying multi-view self-representations for clustering by tensor multi-rank minimization[J]. International Journal of Computer Vision, 2018, 126(11): 1157-1179.\n\n[2]Jia Y, Liu H, Hou J, et al. Multi-view spectral clustering tailored tensor low-rank representation[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2021, 31(12): 4784-4797.\n\n**Q5: The manuscript contains several minor typographical and formatting errors that need to be addressed.**  \n\n**A5:** We have thoroughly reviewed the entire text, corrected all identified errors, and improved language clarity to ensure the quality of the final version of the article."}]

Author rebuttal:

Meta-review: **Summary of Contributions**: The paper proposes a multiview clustering framework through a multi-agent framework. Multiple agents extract features, combined with an aggregator, and finally an LLM-guided dynamic fusion of extracted features for unsupervised learning. Feature learning uses spatial and frequency domain and the LLM dynamically learns a notion of "view quality" and "fusion weights". Empirical evaluation on six benchmark datasets demonstrates strong results. 

**Strengths**: Reviewers noted the following strengths:

1. Novelty of integrating LLMs as a decision-maker and fusion agend for multiview clustering, explicitly overcoming known issues of static strategies.
2. Importance of using frequency domain feature learning to improve robustness to noise.
3. Exhaustive empirical evaluation
4. A few reviewers noted that writing quality, and exposition is well done and the paper is easy to follow (including visualizations aka Figure 1).

**Weaknesses** : Reviewers noted the following weaknesses in their initial review:

1. Dynamic aggregation has been proposed before potentially reducing novelty, where use of LLMs is the main contribution
2. Lack of clarity on whether LLMs need to be fine-tuned for this purpose
3. Not all design choices are well justified, such as choice of scores of "view quality", justification of use of frequency domain feature learning
4. Lack of description and comprehensiveness of experimental data.
3. Minor typos
6. Impact of the choice of LLMs not justified
7. Potentially missed baselines

**Rebuttal and discussion**: Authors added additional justification for dynamic aggregation, compared performance across different LLMs, added dataset descriptions in their response, and baselines suggested by reviewers. Reviewers maintained or improved scores based on the rebuttal. 

**Final recommendation**: Accept. Assuming all additional empirical evaluation is worked into the final manuscript, typos and corrections incorporated, I see that reviewers' major concerns are resolved.
Decision: Accept (poster)