technocracy

Many companies rely on external services to keep their operations running smoothly. However, while third-party vendors help power systems and support day-to-day operations, each new integration adds a potential access point that attackers can target. In 2026, third-party risk influences the speed at which incidents spread, the effectiveness of compliance, and the rate at which teams can recover. To prepare for what lies ahead, it is helpful to understand the current risks and know the steps IT teams […]

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The Question That Started This When you put an AI agent on live infrastructure, a question follows immediately: How do you know what it tells you actually happened? Not whether the AI is capable. Whether the observation it hands you — “BGP session down, here’s why” — reflects what the device actually said. Whether the change it claims to have made actually exists on the device. Whether the signed output you’re trusting as ground truth was collected from […]

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arXiv:2602.09265v1 Announce Type: new Abstract: We present a Galerkin boundary element method for clamped Kirchhoff–Love plates with piecewise smooth boundary. It is a direct method based on the representation formula and requires the inversion of the single-layer operator and an application of the double-layer operator to the Dirichlet data. We present trace approximation spaces of arbitrary order, required for both the Dirichlet data and the unknown Neumann trace. Our boundary element method is quasi-optimal with respect to the […]

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Dropout is a representative regularization technique that stochastically deactivates hidden units during training to mitigate overfitting. In contrast, standard inference executes the full network with dense computation, so its goal and mechanism differ from conditional computation, where the executed operations depend on the input. This paper organizes DynamicGate-MLP into a single framework that simultaneously satisfies both the regularization view and the conditional-computation view. Instead of a random mask, the proposed model learns gates that decide whether to use […]

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Federated Learning (FL) is witnessing wider adoption due to its ability to benefit from large amounts of scattered data while preserving privacy. However, despite its advantages, federated learning suffers from several setbacks that directly impact the accuracy, and the integrity of the global model it produces. One of these setbacks is the presence of malicious clients who actively try to harm the global model by injecting backdoor data into their local models while trying to evade detection. The […]

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arXiv:2603.15568v1 Announce Type: new Abstract: Staged tree models enhance Bayesian networks by incorporating context-specific dependencies through a stage-based structure. In this study, we present a new framework for estimating staged trees using hierarchical clustering on the probability simplex, utilizing simplex basesd divergences. We conduct a thorough evaluation of several distance and divergence metrics including Total Variation, Hellinger, Fisher, and Kaniadakis; alongside various linkage methods such as Ward.D2, average, complete, and McQuitty. We conducted the simulation experiments that reveals […]

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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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arXiv:2602.08350v1 Announce Type: cross Abstract: We study the sample complexity of the best-case Empirical Risk Minimizer in the setting of stochastic convex optimization. We show that there exists an instance in which the sample size is linear in the dimension, learning is possible, but the Empirical Risk Minimizer is likely to be unique and to overfit. This resolves an open question by Feldman. We also extend this to approximate ERMs. Building on our construction we also show that […]

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arXiv:2603.27395v1 Announce Type: cross Abstract: We propose a topological framework for the detection of Hopf bifurcations directly from time series, based on persistent homology applied to phase space reconstructions via Takens embedding within the framework of Topological Data Analysis. The central idea is that changes in the dynamical regime are reflected in the emergence or disappearance of a dominant one-dimensional homological features in the reconstructed attractor. To quantify this behavior, we introduce a simple and interpretable scalar topological […]

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arXiv:2603.28896v1 Announce Type: new Abstract: Feed-forward foundation models for multi-view 3-dimensional (3D) reconstruction have been trained on large-scale datasets of perspective images; when tested on wide field-of-view images, e.g., from a fisheye camera, their performance degrades. Their error arises from changes in spatial positions of pixels due to a non-linear projection model that maps 3D points onto the 2D image plane. While one may surmise that training on fisheye images would resolve this problem, there are far fewer […]

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