arXiv:2603.07014v1 Announce Type: cross Abstract: Regression with distribution-valued responses and Euclidean predictors has gained increasing scientific relevance. While methodology for univariate distributional data has advanced rapidly in recent years, multivariate distributions, which additionally encode dependence across univariate marginals, have received less attention and pose computational and statistical challenges. In this work, we address these challenges with a new regression approach for multivariate distributional responses, in which distributions are modeled within the semiparametric nonparanormal family. By incorporating the nonparanormal […]

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arXiv:2604.09682v1 Announce Type: new Abstract: Autonomous network management in Open Radio Access Networks requires intelligent decision making across conflicting objectives, yet existing LLM based multi agent systems employ homogeneous strategies and lack systematic predeployment validation. We introduce a persona driven multi agent framework where configurable behavioral personas structured specifications encoding optimization priorities, risk tolerance, and decision making style influence five specialized agents (planning, coordination, resource allocation, code generation, analysis). To enable rigorous validation, we develop a three dimensional […]

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The challenge with AI in open source security has transitioned from an AI slop tsunami into more of a … plain security report tsunami. Less slop but lots of reports. Many of them really good. I’m spending hours per day on this now. It’s intense. — Daniel Stenberg, lead developer of cURL Tags: daniel-stenberg, security, curl, generative-ai, ai, llms, ai-security-research

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Driven by the right to be forgotten (RTBF), machine unlearning has become an essential requirement for privacy-preserving machine learning. However, its realization in decentralized federated learning (DFL) remains largely unexplored. In DFL, clients exchange local updates only with neighbors, causing model information to propagate and mix across the network. As a result, when a client requests data deletion, its influence is implicitly embedded throughout the system, making removal difficult without centralized coordination. We propose a novel certified unlearning […]

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Quantum unitary synthesis addresses the problem of translating abstract quantum algorithms into sequences of hardware-executable quantum gates. Solving this task exactly is infeasible in general due to the exponential growth of the underlying combinatorial search space. Existing approaches suffer from misaligned optimization objectives, substantial training costs and limited generalization across different qubit counts. We mitigate these limitations by using supervised learning to approximate the minimum description length of residual unitaries and combining this estimate with stochastic beam search […]

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Author(s): Marc Bara Originally published on Towards AI. Photo by Google DeepMind on Unsplash We have exactly one example of general intelligence: the human brain. We are spending hundreds of billions trying to build another with AI. And we are not copying the one that works. Why? The answer has less to do with science than with which hardware happened to be available. This is not obvious at first glance. Over the last few years, research comparing brains […]

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Last week, while scrolling through security advisories, I stopped cold on a headline from Aikido Security. A threat actor called GlassWorm had compromised at least 151 GitHub repositories between March 3 and March 9. The attack had also spread to npm packages and VS Code extensions. And the payload was invisible. Not obfuscated. Not minified. Literally invisible to the human eye, hidden inside Unicode characters that render as blank space in every code editor on the planet. I […]

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arXiv:2603.28777v1 Announce Type: new Abstract: No matter how much the world of computing changes, system design remains crucial. While most people try to learn it through quick tutorials or AI-generated summaries, there is no better way to master the field than by studying the original research papers. This book serves as a roadmap through those foundational texts, covering seminal papers in distributed systems, operating systems, and big data. It doesn’t just look at what these systems do; it […]

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