arXiv:2603.12289v1 Announce Type: new Abstract: The physical layer (PHY) in wireless communication systems has traditionally relied on model-based methods that are often optimized individually as independent blocks to perform tasks such as modulation, coding, and channel estimation. However, these approaches face challenges when it comes to capturing real-world nonlinearities, hardware imperfections, and increasing complexity in modern networks. This paper surveys advancements in applying deep learning (DL) for end-to-end PHY optimization by incorporating the autoencoder (AE) model as a […]

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arXiv:2601.08005v1 Announce Type: new Abstract: Frontier AI regulations primarily focus on systems deployed to external users, where deployment is more visible and subject to outside scrutiny. However, high-stakes applications can occur internally when companies deploy highly capable systems within their own organizations, such as for automating R&D, accelerating critical business processes, and handling sensitive proprietary data. This paper examines how frontier AI regulations in the United States and European Union in 2025 handle internal deployment. We identify three […]

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A question for people working in RL and image generative models (diffusion, flow based etc). There seems to be more emerging work in RL fine tuning techniques for these models. I’m interested to know – is it crazy to try to train these models from scratch with a reward signal only (i.e without any supervision data)? What techniques could be used to overcome issues with reward sparsity / cold start / training instability? submitted by /u/amds201 [link] [comments]

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[GitHub] platform activity is surging. There were 1 billion commits in 2025. Now, it’s 275 million per week, on pace for 14 billion this year if growth remains linear (spoiler: it won’t.) GitHub Actions has grown from 500M minutes/week in 2023 to 1B minutes/week in 2025, and now 2.1B minutes so far this week. — Kyle Daigle, COO, GitHub Tags: github, github-actions

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Federated active learning (FAL) seeks to reduce annotation cost under privacy constraints, yet its effectiveness degrades in realistic settings with severe global class imbalance and highly heterogeneous clients. We conduct a systematic study of query-model selection in FAL and uncover a central insight: the model that achieves more class-balanced sampling, especially for minority classes, consistently leads to better final performance. Moreover, global-model querying is beneficial only when the global distribution is highly imbalanced and client data are relatively […]

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The discovery rate of optical transients will explode to 10 million public alerts per night once the Vera C. Rubin Observatory’s Legacy Survey of Space and Time comes online, overwhelming the traditional physics-based inference pipelines. A continuous-time forecasting AI model is of interest because it can deliver millisecond-scale inference for thousands of objects per day, whereas legacy MCMC codes need hours per object. In this paper, we propose SELDON, a new continuous-time variational autoencoder for panels of sparse […]

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Thanks to their remarkable flexibility, diffusion models and flow models have emerged as promising candidates for policy representation. However, efficient reinforcement learning (RL) upon these policies remains a challenge due to the lack of explicit log-probabilities for vanilla policy gradient estimators. While numerous attempts have been proposed to address this, the field lacks a unified perspective to reconcile these seemingly disparate methods, thus hampering ongoing development. In this paper, we bridge this gap by introducing a comprehensive taxonomy […]

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Porous materials — natural or engineered — often exhibit dual pore-network structures that govern processes such as mineral exploration and hydrocarbon recovery from tight shales. Double porosity/permeability (DPP) mathematical models describe incompressible fluid flow through two interacting pore networks with inter-network mass exchange. Despite significant advances in numerical methods, there remains a need for computational frameworks that enable rapid forecasting, data assimilation, and reliable inverse analysis. To address this, we present a physics-informed neural network (PINN) framework for […]

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There are hundreds of LLM papers each month proposing new techniques and approaches. However, one of the best ways to see what actually works well in practice is to look at the pre-training and post-training pipelines of the most recent state-of-the-art models. Luckily, four major new LLMs have been released in the last months, accompanied by relatively detailed technical reports. In this article, I focus on the pre-training and post-training pipelines of the following models: Alibaba’s Qwen 2, […]

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arXiv:2603.08712v1 Announce Type: new Abstract: Floating point arithmetic is costly on FPGA platforms due to wide datapaths, normalization, and carry propagation, motivating alternative numerical representations that improve throughput and efficiency. This paper presents the Hybrid Residue Floating Numerical Architecture (HRFNA), a fully specified numerical system that combines carry-free residue arithmetic with lightweight exponent-based scaling to achieve wide dynamic range, predictable error behavior, and efficient FPGA implementation. HRFNA is developed with a rigorous mathematical foundation: the hybrid number space […]

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