In this month’s episode of Functional Futures, our guest is Oscar Spencer – a co-author of a functional programming language called Grain that compiles to WebAssembly. Listen to the episode to learn more about Grain, WebAssembly, and how to sell functional programming to developers. You can check out the full episode on our YouTube channel or listen to the audio version on our podcast page. Below, you can find some of the highlights of the episode, edited for […]

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arXiv:2602.05531v1 Announce Type: cross Abstract: We analyze algorithms for solving stochastic variational inequalities (VI) without the bounded variance or bounded domain assumptions, where our main focus is min-max optimization with possibly unbounded constraint sets. We focus on two classes of problems: monotone VIs; and structured nonmonotone VIs that admit a solution to the weak Minty VI. The latter assumption allows us to solve structured nonconvex-nonconcave min-max problems. For both classes of VIs, to make the expected residual norm […]

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arXiv:2602.10691v1 Announce Type: new Abstract: We study the slice-matching scheme, an efficient iterative method for distribution matching based on sliced optimal transport. We investigate convergence to the target distribution and derive quantitative non-asymptotic rates. To this end, we establish __ojasiewicz-type inequalities for the Sliced-Wasserstein objective. A key challenge is to control along the trajectory the constants in these inequalities. We show that this becomes tractable for Gaussian distributions. Specifically, eigenvalues are controlled when matching along random orthonormal bases […]

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Introduction Disclaimer: This article is a cross post from Pytorch Medium Blog Post. One of the most exciting advancements, that has pushed the frontier of the Artificial Intelligence (AI) in recent years, is Deep Reinforcement Learning (DRL). DRL belongs to the family of machine learning algorithms. It assumes that intelligent machines can learn from their actions similar to the way humans learn from experience. Over the recent years we could witness some impressive real-world applications of DRL. The […]

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Generating a 3D model and printing it are two separate problems. Text-to-3D and image-to-3D tools have made generation fast. Preparing that model for a real FDM printer, splitting it, capping cut surfaces, arranging parts on the build plate, has still required manual work across Blender, Meshmixer, and a slicer. Most models are not print-ready as generated. They exceed build plate dimensions, require color separation across multiple filaments, or contain open mesh surfaces that slicers cannot process. Addressing these […]

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arXiv:2602.22369v1 Announce Type: cross Abstract: This paper considers a non-standard problem of generating samples from a low-temperature Gibbs distribution with emph{constrained} support, when some of the coordinates of the mode lie on the boundary. These coordinates are referred to as the non-regular part of the model. We show that in a “pre-asymptotic” regime in which the limiting Laplace approximation is not yet valid, the low-temperature Gibbs distribution concentrates on a neighborhood of its mode. Within this region, the […]

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arXiv:2601.13776v1 Announce Type: cross Abstract: Orthogonal and 1-Lipschitz neural network layers are essential building blocks in robust deep learning architectures, crucial for certified adversarial robustness, stable generative models, and reliable recurrent networks. Despite significant advancements, existing implementations remain fragmented, limited, and computationally demanding. To address these issues, we introduce Orthogonium , a unified, efficient, and comprehensive PyTorch library providing orthogonal and 1-Lipschitz layers. Orthogonium provides access to standard convolution features-including support for strides, dilation, grouping, and transposed-while maintaining […]

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arXiv:2601.21000v1 Announce Type: new Abstract: Recent advances in large language models (LLMs) have led to strong reasoning capabilities; however, evaluating such models in low-resource languages remains challenging due to the lack of standardized benchmarks. In particular, Urdu reasoning evaluation has been limited by the sensitivity of machine translation and an emphasis on general language tasks rather than reasoning benchmarks. In this paper, we propose a contextually ensembled translation framework with human-in-the-loop validation that leverages multiple translation systems to […]

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