arXiv:2601.08184v2 Announce Type: replace-cross Abstract: Finite-time central limit theorem (CLT) rates play a central role in modern machine learning. In this paper, we study CLT rates for multivariate dependent data in Wasserstein-$p$ ($W_p$) distance, for general $p geq 1$. We focus on two fundamental dependence structures that commonly arise in machine learning: locally dependent sequences and geometrically ergodic Markov chains. In both settings, we establish the first optimal $O(n^{-1/2})$ rate in $W_1$, as well as the first $W_p$ […]

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arXiv:2602.02610v1 Announce Type: new Abstract: Consent is an ethical cornerstone of clinical research and healthcare in general. Although the ethical principles of consent – providing information, ensuring comprehension, and ensuring voluntariness – are well-defined, the technological infrastructure remains outdated. Clinicians are responsible for obtaining informed consent from research subjects or patients, and for managing it before, during, and after clinical trials or care, which is a burden for them. The voluntary nature of participating in clinical research or […]

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We develop a method for training neural networks on Boolean data in which the values at all nodes are strictly $pm 1$, and the resulting models are typically equivalent to networks whose nonzero weights are also $pm 1$. The method replaces loss minimization with a nonconvex constraint formulation. Each node implements a Boolean threshold function (BTF), and training is expressed through a divide-and-concur decomposition into two complementary constraints: one enforces local BTF consistency between inputs, weights, and output; […]

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arXiv:2601.07999v1 Announce Type: new Abstract: In this work, we present a novel perspective on cognitive impairment classification from speech by integrating speech foundation models that explicitly recognize speech dialects. Our motivation is based on the observation that individuals with Alzheimer’s Disease (AD) or mild cognitive impairment (MCI) often produce measurable speech characteristics, such as slower articulation rate and lengthened sounds, in a manner similar to dialectal phonetic variations seen in speech. Building on this idea, we introduce VoxCog, […]

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arXiv:2601.20000v1 Announce Type: new Abstract: In this paper, we propose an adaptive high-order method for hyperbolic systems of conservation laws. The proposed method is based on a dual formulation approach: Two numerical solutions, corresponding to conservative and nonconservative formulations of the same system, are evolved simultaneously. Since nonconservative schemes are known to produce nonphysical weak solutions near discontinuities, we exploit the difference between these two solutions to construct a smoothness indicator (SI). In smooth regions, the difference between […]

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In the training of neural networks, adaptive moment estimation (Adam) typically converges fast but exhibits suboptimal generalization performance. A widely accepted explanation for its defect in generalization is that it often tends to converge to sharp minima. To enhance its ability to find flat minima, we propose its new variant named inverse Adam (InvAdam). The key improvement of InvAdam lies in its parameter update mechanism, which is opposite to that of Adam. Specifically, it computes element-wise multiplication of […]

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arXiv:2603.03301v1 Announce Type: new Abstract: The rapid adoption of large language models (LLMs) has created demand for faster responses and lower costs. Semantic caching, reusing semantically similar requests via their embeddings, addresses this need but breaks classic cache assumptions and raises new challenges. In this paper, we explore offline policies for semantic caching, proving that implementing an optimal offline policy is NP-hard, and propose several polynomial-time heuristics. We also present online semantic aware cache policies that combine recency, […]

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Gemini Robotics ER 1.6: Enhancing spatial reasoning and multi-view understanding for autonomous robotics.

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How infrastructure decisions shaped the AI we’re building 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 and large […]

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