Pillay is an editorial fellow at TIME. Albania's new AI-generated minister Diella speaks during the parliamentary session for the voting of the new government of Albania, in Tirana on Sept. 18, 2025. Albania's new AI-generated minister Diella speaks during the parliamentary session for the voting of the new government of Albania, in Tirana on Sept. 18, 2025. Pillay is an editorial fellow at TIME. It's been three years since the launch of ChatGPT gave hundreds of millions of people access to a kind of digital genie in their pocket--and things have been getting stranger by the month. Besides billions of AI-generated emails and the technology's widespread disruption of education and cognitive work, in 2025, some people began to fall in love with their AIs.

Abstract--Modern Intrusion Detection Systems (IDS) face severe challenges due to heterogeneous network traffic, evolving cyber threats, and pronounced data imbalance between benign and attack flows. While generative models have shown promise in data augmentation, existing approaches are limited to single modalities and fail to capture cross-domain dependencies. This paper introduces MAGE-ID (Multimodal Attack Generator for Intrusion Detection), a diffusion-based generative framework that couples tabular flow features with their transformed images through a unified latent prior . By jointly training Transformer-and CNN-based variational encoders with an EDM-style denoiser, MAGE-ID achieves balanced and coherent multimodal synthesis. Evaluations on CIC-IDS-2017 and NSL-KDD demonstrate significant improvements in fidelity, diversity, and downstream detection performance over T abSyn and T abDDPM, highlighting MAGE-ID's effectiveness for multimodal IDS augmentation.

We study differentially private model training with stochastic gradient descent under learning rate scheduling and correlated noise. Although correlated noise, in particular via matrix factorizations, has been shown to improve accuracy, prior theoretical work focused primarily on the prefix-sum workload. That workload assumes a constant learning rate, whereas in practice learning rate schedules are widely used to accelerate training and improve convergence. We close this gap by deriving general upper and lower bounds for a broad class of learning rate schedules in both single- and multi-epoch settings. Building on these results, we propose a learning-rate-aware factorization that achieves improvements over prefix-sum factorizations under both MaxSE and MeanSE error metrics. Our theoretical analysis yields memory-efficient constructions suitable for practical deployment, and experiments on CIFAR-10 and IMDB datasets confirm that schedule-aware factorizations improve accuracy in private training.

Current studies perceive LLMs' knowledge boundary

Exploring unknown environments efficiently is a fundamental challenge in unsupervised goal-conditioned reinforcement learning.

Understanding reactor-pressure-vessel steel microstructure is crucial for predicting mechanical properties, as carbide precipitates both strengthen the alloy and can initiate cracks. In scanning electron microscopy images, gray-value overlap between carbides and matrix makes simple thresholding ineffective. We present a data-efficient segmentation pipeline using a lightweight U-Net (30.7~M parameters) trained on just \textbf{10 annotated scanning electron microscopy images}. Despite limited data, our model achieves a \textbf{Dice-Sørensen coefficient of 0.98}, significantly outperforming the state-of-the-art in the field of metallurgy (classical image analysis: 0.85), while reducing annotation effort by one order of magnitude compared to the state-of-the-art data efficient segmentation model. This approach enables rapid, automated carbide quantification for alloy design and generalizes to other steel types, demonstrating the potential of data-efficient deep learning in reactor-pressure-vessel steel analysis.

We also show that MatrixNet respects group relations allowing generalization to group elements of greater word length than in the training set.