May 2026

Most infrastructure teams have a moment where someone says “we should build a platform.” The motivation is real: teams are duplicating work, the current setup is hard to use consistently, and a more structured approach would help. A few months later, the platform is a Terraform module collection, a GitLab CI template, a shared repository of scripts, and a README that several people have tried to keep current. That is a useful thing. It is not a platform. […]

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FTC to Require Cox Media Group, Two Other Firms to Pay Nearly $1 Million to Settle Charges They Deceived Customers About “Active Listening” AI-Powered Marketing Service Back in 2024 Cox Media Group were caught trying to sell advertisers packages based on “active listening”, with this deck which claimed: Smart devices capture real-time intent data by listening to our conversations Advertisers can pair this voice-data with behavioral data to target in-market consumers I wrote about this in September 2024. […]

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arXiv:2605.22010v1 Announce Type: new Abstract: We consider one-hidden layer neural networks trained in the feature-learning regime using gradient descent, and relate the output of the finite-width network $f_{hat{rho}_t^m}$ to its infinite-width counterpart $f_{rho_t^{MF}}$, which evolves in the mean-field dynamics. While constant-time horizon bounds for $|f_{rho_t^{MF}} – f_{hat{rho}_t^m}|$ may be obtained via standard Gr”onwall estimates, the long-time behavior of the fluctuation is a more delicate matter. Uniform-in-time bounds often rely on (local) strong convexity in the landscape or Logarithmic […]

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arXiv:2605.21736v1 Announce Type: new Abstract: Logged advertising auctions make offline reserve-price evaluation attractive but risky. Replay tables can identify policies with large apparent yield gains, yet they can also hide weak threshold support, multiple-comparison effects, subgroup harm, and bidder-response uncertainty. Existing replay and off-policy evaluation methods estimate or rank policy values, but they do not directly answer the operational question of whether the available evidence is strong enough to justify validation. This paper develops a support-aware offline decision […]

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arXiv:2605.21557v1 Announce Type: new Abstract: Conventional wisdom holds that large-batch training is fundamentally incompatible with Reinforcement Learning (RL) – beyond a modest threshold, increasing batch sizes typically yields diminishing returns or performance degradation due to the inherent non-stationarity of the data distribution. We challenge this view by observing that non-stationarity is not a fixed property of RL, but evolves throughout training: early stages exhibit rapid behavioral shifts that demand small batches for plasticity, whereas late stages approach a […]

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arXiv:2605.21548v1 Announce Type: new Abstract: We study the problem of selecting covariates for unbiased estimation of the total causal effect.Existing approaches typically rely on global causal structure learning over all variables, or on strong assumptions such as causal sufficiency – where observed variables share no latent confounders – or the pretreatment assumption, which limits covariates to those unaffected by the treatment or outcome. These requirements are often unrealistic in practice, and global learning becomes computationally prohibitive in high-dimensional […]

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arXiv:2605.21534v1 Announce Type: new Abstract: Kolmogorov-Arnold Networks (KANs) approximate multivariate functions using learnable univariate edge functions, typically parameterized by B-spline bases. Although effective, spline-based implementations can be computationally expensive. A modified version of KANs, called FastKAN, improves efficiency by replacing splines with Gaussian radial basis functions (RBFs), but it relies on a fixed kernel and shape parameter. In this work, we extend the RBF-based KAN framework by introducing a broader family of radial basis kernels and by initializing […]

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arXiv:2605.21494v1 Announce Type: new Abstract: Overparametrized models can exhibit an excellent generalization performance, although they should be prone to overfitting according to classical statistical theory. The discovery of the “double descent”, indicating that the generalization error decreases after a certain model complexity has been reached, opened a new line of research. Robust statistics considers statistical estimation on contaminated data, which, due to assumptions that do not hold on real data, let data points appear as outliers w.r.t. the […]

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arXiv:2605.21493v1 Announce Type: new Abstract: The ability to detect out-of-distribution (OOD) inputs is fundamental to safe deployment of machine learning systems. Yet, current methods often rely on feature representations that are optimised solely for classification accuracy, neglecting the distinct requirements of epistemic uncertainty. We introduce GOEN (Geometry-Optimised Epistemic Network), a simple pipeline that combines multi-scale features, L2 normalisation, Mahalanobis distance, and a calibration head trained with real hard OOD examples. Through systematic ablation we uncover a counter-intuitive finding: […]

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