A Structured Debate-Mixture-of-Agents Framework for Complex Clinical Diagnostic Decision Support
本文提出了一种名为DMoA的多智能体框架，通过结构化角色互动支持迭代诊断推理，解决了复杂临床诊断中大型语言模型局限性的问题。
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Attributed Graph Clustering with Multi-Scale Weight-Based Pairwise Coarsening and Contrastive Learning
Existing attributed graph clustering methods suffer from three key challenges: inadequate modeling of long-range dependencies, feature collapse, and structural information loss during graph coarsening. To address these, this paper proposes a multi-scale weighted dual coarsening framework integrated with one-to-many contrastive learning. First, a global similarity-guided edge merging strategy is designed to preserve both long-range dependencies and fine-grained structural details during coarsening. Second, a cluster-center-aware one-to-many contrastive learning objective is introduced to mitigate feature masking caused by high-degree nodes and enhance representation diversity. Third, a multi-scale weight-aware coarsening mechanism is jointly optimized with a graph reconstruction loss and KL-divergence regularization to ensure structural consistency across scales. Extensive experiments on ACM, Citeseer, Cora, and DBLP demonstrate significant and robust performance gains, with the normalized mutual information (NMI) improving by up to 15.24%.
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Camera-based implicit mind reading by capturing higher-order semantic dynamics of human gaze within environmental context
Existing emotion recognition methods suffer from key limitations: overt behavioral cues (e.g., facial expressions, speech) are easily feigned; physiological signals require invasive instrumentation; and gaze analysis often neglects environmental context. To address these, we propose a non-intrusive, continuous emotion recognition paradigm leveraging only a standard high-definition camera to simultaneously capture naturalistic gaze trajectories and head motion. For the first time, our approach deeply integrates gaze dynamics, environmental semantics, and temporal evolution into a unified spatial–semantic–temporal behavioral model. It operates implicitly—requiring no user cooperation or specialized sensors—to decode affective states. Experimental results demonstrate high robustness, real-time performance, low cost, and strong scalability in unconstrained real-world settings. This work advances computational affective science by formalizing emotion as an emergent product of human–environment interaction, offering a novel, scalable, and ecologically valid framework for implicit affective computing.
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Tri-Learn Graph Fusion Network for Attributed Graph Clustering
To address over-smoothing and over-compression issues in Graph Convolutional Networks (GCNs) for attributed graph clustering, this paper proposes a novel tri-channel collaborative learning framework integrating GCNs, autoencoders, and graph Transformers. The framework jointly models node attributes and topological structure via a designed mutual-learning mechanism and a multi-channel feature fusion module, thereby enhancing both global consistency and local discriminability. Unlike monolithic architectures, it enables deep complementarity and joint optimization across the three representation learning paradigms. Extensive experiments on ACM, Reuters, and USPS datasets demonstrate absolute improvements of 0.87%, 14.14%, and 7.58% in clustering accuracy, respectively, outperforming state-of-the-art methods. These results validate the framework’s effectiveness and generalizability in real-world applications such as news categorization.
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A Structured Debate-Mixture-of-Agents Framework for Complex Clinical Diagnostic Decision Support
本文提出了一种名为DMoA的多智能体框架，通过结构化角色互动支持迭代诊断推理，解决了复杂临床诊断中大型语言模型局限性的问题。
0 citationsRead paper
Attributed Graph Clustering with Multi-Scale Weight-Based Pairwise Coarsening and Contrastive Learning
Existing attributed graph clustering methods suffer from three key challenges: inadequate modeling of long-range dependencies, feature collapse, and structural information loss during graph coarsening. To address these, this paper proposes a multi-scale weighted dual coarsening framework integrated with one-to-many contrastive learning. First, a global similarity-guided edge merging strategy is designed to preserve both long-range dependencies and fine-grained structural details during coarsening. Second, a cluster-center-aware one-to-many contrastive learning objective is introduced to mitigate feature masking caused by high-degree nodes and enhance representation diversity. Third, a multi-scale weight-aware coarsening mechanism is jointly optimized with a graph reconstruction loss and KL-divergence regularization to ensure structural consistency across scales. Extensive experiments on ACM, Citeseer, Cora, and DBLP demonstrate significant and robust performance gains, with the normalized mutual information (NMI) improving by up to 15.24%.
0 citationsRead paper
Camera-based implicit mind reading by capturing higher-order semantic dynamics of human gaze within environmental context
Existing emotion recognition methods suffer from key limitations: overt behavioral cues (e.g., facial expressions, speech) are easily feigned; physiological signals require invasive instrumentation; and gaze analysis often neglects environmental context. To address these, we propose a non-intrusive, continuous emotion recognition paradigm leveraging only a standard high-definition camera to simultaneously capture naturalistic gaze trajectories and head motion. For the first time, our approach deeply integrates gaze dynamics, environmental semantics, and temporal evolution into a unified spatial–semantic–temporal behavioral model. It operates implicitly—requiring no user cooperation or specialized sensors—to decode affective states. Experimental results demonstrate high robustness, real-time performance, low cost, and strong scalability in unconstrained real-world settings. This work advances computational affective science by formalizing emotion as an emergent product of human–environment interaction, offering a novel, scalable, and ecologically valid framework for implicit affective computing.
0 citationsRead paper
Tri-Learn Graph Fusion Network for Attributed Graph Clustering
To address over-smoothing and over-compression issues in Graph Convolutional Networks (GCNs) for attributed graph clustering, this paper proposes a novel tri-channel collaborative learning framework integrating GCNs, autoencoders, and graph Transformers. The framework jointly models node attributes and topological structure via a designed mutual-learning mechanism and a multi-channel feature fusion module, thereby enhancing both global consistency and local discriminability. Unlike monolithic architectures, it enables deep complementarity and joint optimization across the three representation learning paradigms. Extensive experiments on ACM, Reuters, and USPS datasets demonstrate absolute improvements of 0.87%, 14.14%, and 7.58% in clustering accuracy, respectively, outperforming state-of-the-art methods. These results validate the framework’s effectiveness and generalizability in real-world applications such as news categorization.
0 citationsRead paper