Deep learning models for drug--target interaction (DTI) prediction often achieve strong benchmark performance without necessarily relying on mechanistically meaningful molecular features, a limitation that standard accuracy-based evaluation cannot detect. We introduce ISAAC (Intervention-based Structural Auditing Approach for Causal Reasoning), a post-hoc framework that evaluates prior-relative structural sensitivity by probing frozen models through matched mechanistic and spurious input-level interventions, independently of predictive accuracy. Applied to three sequence-based DTI architectures on the Davis benchmark, ISAAC reveals approximately 25\% relative differences in reasoning scores across models with comparable AUROC (within around 3\%), stable across training and intervention seeds and two distinct perturbation operators. These discrepancies, undetectable under conventional accuracy metrics, motivate the use of post-hoc structural auditing as a complement to standard performance evaluation in scientific machine learning for molecular modeling.

Artificial intelligence-generated content (AIGC) has emerged as a transformative paradigm for automating the creation of diverse and customized content, giving rise to rapidly growing computational workloads in cloud data centers. It is imperative for AIGC service providers (ASPs) to strategically schedule AIGC workloads to reduce data center energy costs while guaranteeing high-quality content generation. However, the distinctive characteristics of AIGC services pose critical challenges, including model heterogeneity across ASPs, implicit service quality evaluation, and complex inference process control. To tackle these challenges, we propose a joint energy management and coordinated AIGC workload scheduling framework, which introduces an explicit mathematical characterization of service quality to promote both job transfer among ASPs and fine-grained inference process configuration. Moreover, various energy resources within data centers are jointly considered to enhance power usage flexibility. Subsequently, a system utility maximization problem is formulated to balance AIGC service revenue with operational penalties and costs. Nevertheless, the strong coupling among job scheduling decisions induces severe reward sparsity, which limits the effectiveness of existing deep reinforcement learning (DRL) algorithms. To address this issue, we develop a diffusion model-aided reward shaping approach to synthesize complementary reward signals through a multi-step denoising process. This approach is seamlessly integrated with DRL to enable efficient learning of scheduling policies under sparse environmental feedback. Experiments based on real-world models and datasets demonstrate that our scheme effectively accommodates electricity price fluctuations and AIGC model heterogeneity, while achieving superior learning convergence and system utility compared with benchmark methods.

This manuscript contains preprint of a chapter under consideration for inclusion in the forthcoming third edition of {\em Cover and Thomas's Elements of Information Theory}, posted with permission from Wiley. The table of contents EIT-3 ToC of the new edition can be found at: https://docs.google.com/document/d/1L-m4oQEJw1PJhoxBeMwrrBD8S_HmvzMEkPbYvS24980/edit?usp=sharing . For feedback, please contact abbas@ee.stanford.edu Learning and information theory intersect in both model training and the characterization of fundamental performance limits. This manuscript provides a concise and accessible treatment of the first intersection, requiring only basic background in information theory and statistics at the senior undergraduate or first-year graduate level. End-of-chapter exercises make the material well suited for classroom use as well as self-study. The chapter focuses on the role of divergence measures in model training, with examples ranging from linear and logistic regression to autoregressive models, variational autoencoders, diffusion models, generative adversarial networks, and score-based models. It introduces the evidence lower bound (ELBO), $f$\!-divergences, and the Fisher divergence. In particular, the treatment of the generative diffusion model provides a more systematic and explicit derivation than is typical in the literature.

At the core of scientific computing and much of modern machine learning (ML) lies the challenge of estimating the eigenvalues of high-dimensional Hermitian matrices. Such matrices, including kernels, Hessians, and graph representations, encode the intrinsic geometry and connectivity of the data and models built on them, rendering the pursuit of efficient spectral techniques a primary concern for both theory and practice. Studying eigenspectra has become a prominent approach to understanding performance and guiding training in deep learning [10, 20, 36, 53]. In many cases, the spectra of such matrices have non-trivial structure, often containing spikes, multiple multi-modal bulks, and heavy-tails [14, 25]. Conventional algorithms to extract eigenvalue information from these matrices have required that the data are able to be stored in memory, scratch space, or can at least be accessed as an implicit operator (via matrix-vector products). More recently, a new class of algorithms has emerged that is able to provide highly-accurate estimates of the eigenvalues (or summary functionals thereof [2]) of matrices, even without implicit or explicit access to the full matrix, i.e., of so-called impalpable matrices [1]. One such method, termed Free Decompression (FD), shows great promise as a tool for gaining access to the spectral distributions of such impalpable matrices. The central premise is that by appropriately sampling a small sub-matrix from the large impalpable matrix of interest, one can evolve a partial differential equation (PDE) in the Stieltjes transform of a spectral density in the decompression ratio to the desired matrix dimension.

Multi Armed Bandit (MAB) algorithms are a cornerstone of reinforcement learning and have been studied both theoretically and numerically. One of the most commonly used implementation uses a softmax mapping to prescribe the optimal policy and served as the foundation for downstream algorithms, including REINFORCE. Distinct from vanilla approaches, we consider here the L2 regularized softmax policy gradient where a quadratic term is subtracted from the mean reward. Previous studies exploiting convexity failed to identify a suitable theoretical framework to analyze its convergence when the regularization parameter vanishes. We prove here theoretical convergence results and confirm empirically that this regime makes the L2 regularization numerically advantageous on standard benchmarks.

Survival analysis on tabular data is a well-studied problem. However, existing deep learning methods are often highly task-specific, which can limit the transfer of new approaches from other domains and introduce constraints that may affect performance. We propose TabSurv, an approach that adapts modern tabular architectures to survival analysis using either the Weibull distribution or non-parametric survival prediction. TabSurv optimizes SurvHL, a novel histogram loss function supporting censored data. In addition to a baseline feed-forward network, we implement deep ensembles of MLPs for survival analysis within TabSurv. In contrast to prior work, the ensemble components are trained in parallel, optimizing survival distribution parameters before averaging, which promotes diversity across ensemble component predictions. We perform a comprehensive empirical evaluation of different proposed architectures on 10 diverse real-world survival datasets. Our results show that TabSurv consistently outperforms on average established classical and deep learning baselines, such as RSF, DeepSurv, DeepHit, SurvTRACE. Notably, deep ensembles with Weibull parametrization instead of non-parametric models achieve the highest average rank by C-index. Overall, our study clarifies how modern tabular neural networks can be adapted and trained to tackle survival analysis problems, offering a strong and reliable approach. The TabSurv implementation is publicly available.

Xbox is ditching Microsoft's Copilot AI Xbox is ditching Microsoft's Copilot AI Microsoft announced plans to start stripping Copilot out of select Windows apps in March after criticism of the company's mishandling of its operating system reached a fever pitch. As it turns out though, Windows isn't the only place where you'll see less Copilot: Xbox CEO Asha Sharma has announced that the AI assistant will also be removed from the gaming brand's mobile app and Xbox consoles. Under previous Xbox leadership, Copilot was introduced as a sort of in-game assistant that would be aware of what you're playing and able to offer contextual advice based on what's on your screen. Microsoft launched a beta version of the experience by adding Copilot to the Xbox mobile app in May 2025, but based on a GDC presentation the company gave in March, the plan was to also bring Copilot to Xbox consoles later this year. Those plans apparently don't align with where Xbox is headed, Sharma said in a post announcing new hires to the Xbox division.

PCWorld reports the update delivers 52.5% fewer hallucinations and 37.3% fewer inaccurate claims while providing more concise answers. Enhanced features include improved context integration from previous chats, files, and Gmail, plus transparency showing which memory sources influenced responses. One reason I took a break from ChatGPT a few months ago (I'm back now) was how sick to death I got of its constant emojis, especially when it came to lists. The brain emoji was my least favorite, along with the green checkmarks, the pointy fingers, and the yellow "hazard" signs. Well, I'll believe it when I see it, but with its latest "instant" model, OpenAI promises that we'll be getting way less of those "gratuitous" emojis in ChatGPT's responses.

New artificial intelligence (AI) tools and capabilities from Google, Microsoft and xAI will now be tested by the US Department of Commerce before they are released to the public. The tech firms have agreed to voluntarily submit their models for testing through Commerce's Center for AI Standards and Innovation (CAISI). The new pacts are an expansion on agreements by AI companies like OpenAI and Anthropic that were reached during the Biden Administration, and will see AI models from all of the companies evaluated for their capabilities and security. These expanded industry collaborations help us scale our work in the public interest at a critical moment, CAISI's director Chris Fall said. Overall, the evaluations of the AI tools will cover testing, collaborative research and best practice development related to commercial AI systems.

Plus: The Pentagon has struck sweeping AI deals for classified work. Week one of the Musk v. Altman trial: what it was like in the room Two of the most powerful figures in AI--Sam Altman and Elon Musk--are in the middle of a landmark legal showdown, with Musk alleging he was misled about OpenAI becoming a for-profit company. Our reporter Michelle Kim, who also happens to be a lawyer, has been in court each day, and has broken down the first week's key moments in her latest report . In a new Q&A, she also reveals what it was like in the room, the new details that have emerged about how Musk and OpenAI operate--and what we can expect from this week's proceedings. Find out what she's discovered so far, and if you want to keep up with MIT Technology Review's ongoing coverage of the Musk v. Altman trial, follow @techreview or @michelletomkim on X. Faster than many realize, AI is becoming the primary interface through which we form beliefs and participate in democratic self-governance. This shift could further strain already fragile institutions, but it could also help address problems like polarization and declining civic engagement.