arXiv:2309.10370v4 Announce Type: replace-cross Abstract: In this paper, we approach the problem of cost (loss) minimization in underparametrized shallow ReLU networks through the explicit construction of upper bounds which appeal to the structure of classification data, without use of gradient descent. A key focus is on elucidating the geometric structure of approximate and precise minimizers. We consider an $L^2$ cost function, input space $mathbb{R}^M$, output space ${mathbb R}^Q$ with $Qleq M$, and training input sample size that can […]

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arXiv:2507.04754v2 Announce Type: replace Abstract: In designing generative models, it is commonly believed that in order to learn useful latent structure, we face a fundamental tension between expressivity and structure. In this paper we challenge this view by proposing a new approach to training arbitrarily expressive generative models that simultaneously learn causally disentangled concepts. This is accomplished by adding a simple context module to an arbitrarily complex black-box model, which learns to process concept information by implicitly inverting […]

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Weight-space models learn directly from the parameters of neural networks, enabling tasks such as predicting their accuracy on new datasets. Naive methods — like applying MLPs to flattened parameters — perform poorly, making the design of better weight-space architectures a central challenge. While prior work leveraged permutation symmetries in standard networks to guide such designs, no analogous analysis or tailored architecture yet exists for Kolmogorov-Arnold Networks (KANs). In this work, we show that KANs share the same permutation […]

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arXiv:2601.10768v1 Announce Type: new Abstract: This paper investigates complex product-innovation processes using models grounded in a set of heuristics. Each heuristic is expressed through simple trends — increasing, decreasing, or constant — which serve as minimally information-intensive quantifiers, avoiding reliance on numerical values or rough sets. A solution to a trend model is defined as a set of scenarios with possible transitions between them, represented by a transition graph. Any possible future or past behaviour of the system […]

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AI video generation has quietly become one of the most powerful creative tools on the internet. What once required a studio, expensive software, and professional editors can now be accomplished with prompts, reference images, and a browser tab. At the center of this shift is DomoAI, a complete AI Video Generator that combines animation, editing, upscaling, and stylization into one seamless workflow. For creators who want speed, flexibility, and visual impact, learning this platform can completely reshape how […]

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arXiv:2602.07065v1 Announce Type: new Abstract: Contactless and non-invasive estimation of mechanical properties of physical media from optical observations is of interest for manifold engineering and biomedical applications, where direct physical measurements are not possible. Conventional approaches to the assessment of image displacement and non-contact material probing typically rely on time-consuming iterative algorithms for non-rigid image registration and constitutive modelling using discretization and iterative numerical solving techniques, such as Finite Element Method (FEM) and Finite Difference Method (FDM), which […]

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arXiv:2604.15376v1 Announce Type: new Abstract: Multi-step zoom-in pipelines are widely used for GUI grounding, yet the intermediate predictions they produce are typically discarded after coordinate remapping. We observe that these intermediate outputs contain a useful confidence signal for free: zoom consistency, the distance between a model’s step-2 prediction and the crop center. Unlike log-probabilities or token-level uncertainty, zoom consistency is a geometric quantity in a shared coordinate space, making it directly comparable across architecturally different VLMs without calibration. […]

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arXiv:2603.03457v1 Announce Type: new Abstract: Over the last two decades, research funders have adopted Open Access (OA) mandates, with various forms and success. While some funders emphasize gold OA through article processing charges, others favour green OA and repositories, leading to a fragmented policy landscape. Compliance with these mandates depends on several factors, including disciplinary field, monitoring, and availability of repository infrastructure. Based on 5 million papers supported by 36 funders from 20 countries, 11 million papers funded […]

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arXiv:2601.20071v1 Announce Type: new Abstract: Gradient-regularized value learning methods improve sample efficiency by leveraging learned models of transition dynamics and rewards to estimate return gradients. However, existing approaches, such as MAGE, struggle in stochastic or noisy environments, limiting their applicability. In this work, we address these limitations by extending distributional reinforcement learning on continuous state-action spaces to model not only the distribution over scalar state-action value functions but also over their gradients. We refer to this approach as […]

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