Selected work

Representative Papers

Beyond Independence: Learning Correlated Views for Variational Incomplete Multi-View Clustering

Aug 16, 2026

This study addresses the limitation of conditional view independence in existing variational incomplete multi-view clustering methods by proposing a novel variational framework that explicitly models cross-view correlations. By parameterizing the covariance matrix via normalized Cholesky decomposition, the approach adaptively captures intrinsic data structures through posterior estimation errors under a unified optimization objective. This method overcomes the independence bottleneck with minimal additional parameters and consistently outperforms state-of-the-art baselines across multiple benchmark datasets. These results effectively validate the critical role of explicit cross-view correlation modeling in enhancing clustering performance for incomplete multi-view learning scenarios.

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IO Factory: Simulating AI-Enabled Influence Campaigns at Scale

Aug 11, 2026

This study addresses the challenge of detecting AI-driven coordinated influence operations, which are difficult to identify through isolated data points. To this end, the authors propose a traceable and reproducible closed-loop simulation framework that models influence campaigns as an end-to-end process encompassing roles, actions, exposure, evaluation, and adaptation. Built upon a multi-agent system, the framework integrates action planning with feedback-driven adaptation mechanisms and incorporates belief variables alongside structured assessment methodologies. The system was successfully deployed at scale, simulating over 100,000 agents and generating auditable exposure pathways and belief evolution trajectories. This work represents the first large-scale simulation capable of capturing the full lifecycle of AI-coordinated influence operations, demonstrating both the scalability of the architecture and its analytical efficacy.

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Latest Papers

Beyond Independence: Learning Correlated Views for Variational Incomplete Multi-View Clustering

Aug 16, 2026

This study addresses the limitation of conditional view independence in existing variational incomplete multi-view clustering methods by proposing a novel variational framework that explicitly models cross-view correlations. By parameterizing the covariance matrix via normalized Cholesky decomposition, the approach adaptively captures intrinsic data structures through posterior estimation errors under a unified optimization objective. This method overcomes the independence bottleneck with minimal additional parameters and consistently outperforms state-of-the-art baselines across multiple benchmark datasets. These results effectively validate the critical role of explicit cross-view correlation modeling in enhancing clustering performance for incomplete multi-view learning scenarios.

0 citationsRead paper

IO Factory: Simulating AI-Enabled Influence Campaigns at Scale

Aug 11, 2026

This study addresses the challenge of detecting AI-driven coordinated influence operations, which are difficult to identify through isolated data points. To this end, the authors propose a traceable and reproducible closed-loop simulation framework that models influence campaigns as an end-to-end process encompassing roles, actions, exposure, evaluation, and adaptation. Built upon a multi-agent system, the framework integrates action planning with feedback-driven adaptation mechanisms and incorporates belief variables alongside structured assessment methodologies. The system was successfully deployed at scale, simulating over 100,000 agents and generating auditable exposure pathways and belief evolution trajectories. This work represents the first large-scale simulation capable of capturing the full lifecycle of AI-coordinated influence operations, demonstrating both the scalability of the architecture and its analytical efficacy.

0 citationsRead paper