Multiple clustering aims to uncover diverse latent structures within the data, enabling a more comprehensive understanding of complex datasets. However, existing approaches either heavily rely on user-supplied keywords or disregard user-interested clustering types, limiting the ability to discover the full range of explainable clusterings of interests, particularly in high-dimensional settings. Furthermore, existing methods insufficiently leverage the rich textual semantics and fall short in fully integrating multi-modal information. To address these challenges, we propose MLLM enriched Multiple Clustering (MLLMMC), a novel framework that leverages multi-modal large language model (MLLM) to explore explainable non-redundant clustering. Specifically, MLLMMC first employs MLLM to generate sample descriptions, which serve as input for LLM to perform prompt-driven reasoning and infer latent clustering types, and then merges them with user-interested types to obtain diverse and explainable clustering types. For each selected type, MLLMMC utilizes MLLM to generate sample-level textual descriptions and aligns them with corresponding visual features through a cross-attention fusion module, which produces a semantically aligned and enriched representation for the target clustering type. Extensive experiments on six benchmark datasets from diverse domains demonstrate that MLLMMC achieves diverse, explainable, and high-quality clustering outcomes, outperforming state-of-the-art multiple clustering methods with a large margin.

Contrastive Language-Image Pre-training (CLIP) has emerged as an effective framework for multi-modal representation learning, achieving notable success in diverse tasks such as medical image analysis. CLIP's growing prominence in medical image applications is restricted by its significant computational demands, creating implementation challenges in resource-constrained clinical environments. While knowledge distillation offers an effective approach for model compression with preserved accuracy, existing methods suffer from two fundamental limitations. Firstly, existing methods focus on learning better information from single models while ignoring the fact that student models can generalize well under the guidance of multiple teachers. Secondly, they overlook the complementary information in the CLIP model where the text encoder and image encoder can be leveraged as heterogeneous information to teach one single modality. To tackle these challenges, we propose an Adaptive Heterogeneous Multi-teacher Knowledge Distillation (AdaHet-MKD) framework for effective knowledge transfer across heterogeneous text-image models and among multiple teacher models. The key innovations include: (i) adaptively determining the contribution of each teacher model to specific instances, thereby generating integrated soft logits, and (ii) enabling the student model to operate independently of the teacher model's architecture, which enhances flexibility in teacher-student pairings. Experimental evaluations on publicly available medical datasets demonstrate that our approach has achieved the state-of-the-art performance compared to baselines.

Soft Actor-Critic (SAC) and other off-policy reinforcement learning methods have emerged as powerful tools for complex dynamic decision-making. However, they are susceptible to detrimental Q-value overestimation bias in stochastic environments. This bias critically impairs policy optimization, propagates estimation errors, and can lead to suboptimal policies. To address this challenge, we introduce Soft Actor-Critic with Bias (SAC-B), a novel framework designed to mitigate Q-value overestimation while preserving the strengths of maximum entropy RL. SAC-B incorporates a dynamic bias compensator, implemented as a learnable network, to heuristically counter Q-value overestimation. Crucially, we employ an error-decoupled stabilized architecture that isolates this bias prediction module from the critic networks, thereby eliminating error propagation and enhancing stability. Furthermore, SAC-B utilizes a hybrid objective function that strategically balances temporal difference learning with entropy regularization, enabling rapid adaptation. These synergistic components collectively enable SAC-B to effectively reduce temporal bootstrapping errors while maintaining the desirable exploration properties of the original SAC algorithm. Experiments across six continuous control benchmarks, including BipedalWalkerHardcore-v3 and Quadruped, validate the superiority and enhanced stability of our approach compared to existing methods.

Recent research in cooperative Multi-Agent Reinforcement Learning (MARL) has shown significant interest in utilizing Graph Neural Networks (GNNs) for communication learning due to their strong ability to process feature and topological information of agents into message representations for downstream action selection and coordination. However, GNNs generally assume network homogeneity that nodes of the same class tend to be interconnected. In real-world multi-agent systems, such assumptions are often unrealistic, as agents within the same class can be distant from each other. Furthermore, GNN-based MARL methods overlook the crucial role of feature similarity of agents in action coordination, which also restricts their performance. To overcome these limitations, we propose a Multi-Agent communication mechanism with Information preserving graph contrastive Learning (MAIL), which enhances message representation by preserving the comprehensive features of adjacent agents while integrating topological information. Specifically, MAIL considers three distinct graph views: original view, agent feature view, and global topological view. MAIL performs contrastive learning across three views to extract comprehensive information. MAIL effectively learns robust and expressive message representations for downstream tasks. Extensive experiments across various environments demonstrate that MAIL outperforms existing GNN-based MARL methods.

Incomplete multi-view data presents a significant challenge for multi-view clustering (MVC). Existing incomplete MVC solutions commonly rely on data imputation to convert incomplete data into complete data. However, this paradigm suffers from the risk of error accumulation when clustering unreliable imputed data, causing suboptimal clustering performance. Moreover, using imputation to fulfill missing data is inefficient, while inferring data categories based solely on the existing views is extremely challenging. To this end, we propose an Imputation-free Incomplete MVC (I2MVC) via pseudo-supervised knowledge distillation. Specifically, I2MVC decomposes the incomplete MVC problem into two tasks: an MVC task for complete data and a pseudo-supervised classification task for fully incomplete data. A self-supervised simple contrastive Teacher network is trained for clustering complete data, and its knowledge is distilled into a lightweight pseudo-supervised Student network. The Student network, unrestricted by view completeness, further guides the clustering of fully incomplete data. Finally, the clustering results from both tasks are merged to generate the final clustering outcome. Experimental results on benchmark datasets demonstrate the effectiveness of I2MVC.

Deep graph clustering (DGC) aims to partition graph nodes into distinct clusters in an unsupervised manner. Despite rapid advancements in this field, DGC remains inherently challenging due to the absence of ground-truth, which complicates the design of effective algorithms and impedes the establishment of standardized benchmarks. The lack of unified datasets, evaluation protocols, and metrics further exacerbates these challenges, making it difficult to systematically assess and compare DGC methods. To address these limitations, we introduce $\texttt{DGCBench}$, the first comprehensive and unified benchmark for DGC methods. It evaluates 12 state-of-the-art DGC methods across 12 datasets from diverse domains and scales, spanning 6 critical dimensions: $\textbf{discriminability}$, $\textbf{effectiveness}$, $\textbf{scalability}$, $\textbf{efficiency}$, $\textbf{stability}$, and $\textbf{robustness}$. Additionally, we develop $\texttt{PyDGC}$, an open-source Python library that standardizes the DGC training and evaluation paradigm. Through systematic experiments, we reveal persistent limitations in existing methods, specifically regarding the homophily bottleneck, training instability, vulnerability to perturbations, efficiency plateau, scalability challenges, and poor discriminability, thereby offering actionable insights for future research. We hope that $\texttt{DGCBench}$, $\texttt{PyDGC}$, and our analyses will collectively accelerate the progress in the DGC community. The code is available at https://github.com/Marigoldwu/PyDGC.

Communication learning is an important research direction in the multiagent reinforcement learning (MARL) domain. Graph neural networks (GNNs) can aggregate the information of neighbor nodes for representation learning. In recent years, several MARL methods leverage GNN to model information interactions between agents to coordinate actions and complete cooperative tasks. However, simply aggregating the information of neighboring agents through GNNs may not extract enough useful information, and the topological relationship information is ignored. To tackle this difficulty, we investigate how to efficiently extract and utilize the rich information of neighbor agents as much as possible in the graph structure, so as to obtain high-quality expressive feature representation to complete the cooperation task. To this end, we present a novel GNN-based MARL method with graphical mutual information (MI) maximization to maximize the correlation between input feature information of neighbor agents and output high-level hidden feature representations. The proposed method extends the traditional idea of MI optimization from graph domain to multiagent system, in which the MI is measured from two aspects: agent features information and agent topological relationships. The proposed method is agnostic to specific MARL methods and can be flexibly integrated with various value function decomposition methods. Considerable experiments on various benchmarks demonstrate that the performance of our proposed method is superior to the existing MARL methods.

Efficient communication learning among agents has been shown crucial for cooperative multi-agent reinforcement learning (MARL), as it can promote the action coordination of agents and ultimately improve performance. Graph neural network (GNN) provide a general paradigm for communication learning, which consider agents and communication channels as nodes and edges in a graph, with the action selection corresponding to node labeling. Under such paradigm, an agent aggregates information from neighbor agents, which can reduce uncertainty in local decision-making and induce implicit action coordination. However, this communication paradigm is vulnerable to adversarial attacks and noise, and how to learn robust and efficient communication under perturbations has largely not been studied. To this end, this paper introduces a novel Multi-Agent communication mechanism via Graph Information bottleneck (MAGI), which can optimally balance the robustness and expressiveness of the message representation learned by agents. This communication mechanism is aim at learning the minimal sufficient message representation for an agent by maximizing the mutual information (MI) between the message representation and the selected action, and simultaneously constraining the MI between the message representation and the agent feature. Empirical results demonstrate that MAGI is more robust and efficient than state-of-the-art GNN-based MARL methods.

Information sharing through communication is essential for tackling complex multi-agent reinforcement learning tasks. Many existing multi-agent communication protocols can be viewed as instances of message passing graph neural networks (GNNs). However, due to the significantly limited expressive ability of the standard GNN method, the agent feature representations remain similar and indistinguishable even though the agents have different neighborhood structures. This further results in the homogenization of agent behaviors and reduces the capability to solve tasks effectively. In this paper, we propose a multi-agent communication protocol via identity-aware learning (IDEAL), which explicitly enhances the distinguishability of agent feature representations to break the diversity bottleneck. Specifically, IDEAL extends existing multi-agent communication protocols by inductively considering the agents' identities during the message passing process. To obtain expressive feature representations for a given agent, IDEAL first extracts the ego network centered around that agent and then performs multiple rounds of heterogeneous message passing, where different parameter sets are applied to the central agent and the other surrounding agents within the ego network. IDEAL fosters expressive communication between agents and generates distinguishable feature representations, which promotes action diversity and individuality emergence. Experimental results on various benchmarks demonstrate IDEAL can be flexibly integrated into various multi-agent communication methods and enhances the corresponding performance.

Recent research on multi-agent reinforcement learning (MARL) has shown that action coordination of multi-agents can be significantly enhanced by introducing communication learning mechanisms. Meanwhile, graph neural network (GNN) provides a promising paradigm for communication learning of MARL. Under this paradigm, agents and communication channels can be regarded as nodes and edges in the graph, and agents can aggregate information from neighboring agents through GNN. However, this GNN-based communication paradigm is susceptible to adversarial attacks and noise perturbations, and how to achieve robust communication learning under perturbations has been largely neglected. To this end, this paper explores this problem and introduces a robust communication learning mechanism with graph information bottleneck optimization, which can optimally realize the robustness and effectiveness of communication learning. We introduce two information-theoretic regularizers to learn the minimal sufficient message representation for multi-agent communication. The regularizers aim at maximizing the mutual information (MI) between the message representation and action selection while minimizing the MI between the agent feature and message representation. Besides, we present a MARL framework that can integrate the proposed communication mechanism with existing value decomposition methods. Experimental results demonstrate that the proposed method is more robust and efficient than state-of-the-art GNN-based MARL methods.

Communication learning is an effective way to solve complicated cooperative tasks in multi-agent reinforcement learning (MARL) domain.Graph neural network (GNN) has been widely adopt for learning the multi-agent communication and various GNN-based MARL methods have emerged.However, most of these methods are not specially designed for heterogeneous multi-agent scenarios, where agents have heterogeneous attributes or features based on different observation spaces or action sets.Without effective processing and transmission of heterogeneous feature information, communication learning will be useless and even reduce the performance of cooperation.To solve this problem, we propose a communication learning mechanism based on heterogeneous GNN and graph information maximization to learn effective communication for heterogeneous agents.Specifically, we use heterogeneous GNN for learning the efficient message representations, which aggregate the local feature information of neighboring agents.Furthermore, we maximize the mutual information (MI) between message representations and local values to make efficient use of information.Besides, we present a MARL framework that can flexibly integrate the proposed communication mechanism with existing value factorization methods.Experiments on various heterogeneous multi-agent scenarios demonstrate the effectiveness and superiority of the proposed method compared with baselines.

Most recent research on multiagent reinforcement learning (MARL) has explored how to deploy cooperative policies for homogeneous agents. However, realistic multiagent environments may contain heterogeneous agents that have different attributes or tasks. The heterogeneity of the agents and the diversity of relationships cause the learning of policy excessively tough. To tackle this difficulty, we present a novel method that employs a heterogeneous graph attention network to model the relationships between heterogeneous agents. The proposed method can generate an integrated feature representation for each agent by hierarchically aggregating latent feature information of neighbor agents, with the importance of the agent level and the relationship level being entirely considered. The method is agnostic to specific MARL methods and can be flexibly integrated with diverse value decomposition methods. We conduct experiments in predator-prey and StarCraft Multiagent Challenge (SMAC) environments, and the empirical results demonstrate that the performance of our method is superior to existing methods in several heterogeneous scenarios.