arXiv:2603.20283v1 Announce Type: new Abstract: Graph neural network (GNN)-based federated recommendation systems effectively capture user-item relationships while preserving data privacy. However, existing methods often face slow convergence on graph data and privacy leakage risks during collaboration. To address these challenges, we propose FastPFRec (Fast Personalized Federated Recommendation with Secure Sharing), a novel framework that enhances both training efficiency and data security. FastPFRec accelerates model convergence through an efficient local update strategy and introduces a privacy-aware parameter sharing mechanism […]

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arXiv:2510.12416v5 Announce Type: replace Abstract: This paper studies how geopolitical and geoeconomic shocks transmit to sovereign risk. Using a daily panel of CDS spreads, financial variables, and news-based indicators for 42 advanced and emerging economies over 2018-2025, we estimate nonlinear Machine Learning models that capture the interactions and threshold effects through which these shocks operate. A Shapley-Taylor decomposition exactly partitions predicted spreads into four channels: Direct, Global Financial Cycle, Uncertainty, and Local. The decomposition reveals a structural distinction. […]

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In today’s cloud-native and AI-driven enterprise landscape, system failures are no longer caused by simple outages but by complex interactions between microservices, automation, and machine-learning models. To understand how explainable AI can transform reliability engineering, we spoke with Adithya Jakkaraju who authored the IEEE International Conference on Advances in Next-Generation Computer Science (ICANCS) 2025 Best Paper, “Explainable AI for Resilient Microservices: A Transparency-Driven Approach,” which presents a practical framework for building trustworthy, auditable AI-driven resilience in large-scale systems. […]

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To teach robots complex manipulation tasks, it is now a common practice to fine-tune a pre-trained vision-language-action model (VLA) on task-specific data. However, since this recipe updates existing representations, it is unsuitable for long-term operation in the real world, where robots must continually adapt to new tasks and environments while retaining the knowledge they have already acquired. Existing continual learning methods for robotics commonly require storing previous data (exemplars), struggle with long task sequences, or rely on task […]

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arXiv:2510.13060v2 Announce Type: replace-cross Abstract: Reverse Kullback-Leibler (KL) divergence-based regularization with respect to a fixed reference policy is widely used in modern reinforcement learning to preserve the desired traits of the reference policy and sometimes to promote exploration (using uniform reference policy, known as entropy regularization). Beyond serving as a mere anchor, the reference policy can also be interpreted as encoding prior knowledge about good actions in the environment. In the context of alignment, recent game-theoretic approaches have […]

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The B2B space is filled with analytical audiences, system thinkers, operators, and decision-makers who need useful information to act. But most content written for this audience does the opposite. Instead of enabling decisions, it focuses on providing intelligent data, insights, and explanations that may signal expertise but fail to guide action. Effective content, therefore, is not defined by how much it explains, but by how efficiently it compresses time-to-value and directs the reader toward a clear outcome. In […]

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Equality saturation explores equivalent program expressions via e-graphs, and its final step—extraction—selects one representative per equivalence class to form an output tree. Standard extraction minimizes a decomposable, single-node cost function that cannot capture multi-node structural patterns exploited by downstream systems such as SMT preprocessors and compiler backends. We formalize pattern-aware extraction as a weighted pattern cover problem on AND-OR directed acyclic graphs and establish its correspondence to tree covering in compiler instruction selection. Three challenges arise: annotation ambiguity […]

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