Shuffling strategies for stochastic gradient descent (SGD), including incremental gradient, shuffle-once, and random reshuffling, are supported by rigorous convergence analyses for arbitrary within-epoch permutations. In particular, random reshuffling is known to improve optimization constants relative to cyclic and shuffle-once schemes. However, existing theory offers limited guidance on how to design new data-ordering schemes that further improve optimization constants or stability beyond random reshuffling. In this paper, we design a pipeline using a large language model (LLM)-guided program evolution […]

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arXiv:2603.26678v1 Announce Type: new Abstract: AI and renewable energy are increasingly framed as a “power couple” — the idea that surging AI electricity demand will accelerate clean-energy investment — yet concerns persist that AI will instead entrench fossil-fuel carbon lock-in. We reconcile these views by modeling the equilibrium interaction between AI growth and renewable investment. In a parsimonious game, a policymaker invests in renewable capacity available to AI and an AI developer chooses capability; the equilibrium depends on […]

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arXiv:2602.02619v1 Announce Type: new Abstract: While Large Language Models (LLMs) excel at short-term tasks, scaling them to long-horizon agentic workflows remains challenging. The core bottleneck lies in the scarcity of training data that captures authentic long-dependency structures and cross-stage evolutionary dynamics–existing synthesis methods either confine to single-feature scenarios constrained by model distribution, or incur prohibitive human annotation costs, failing to provide scalable, high-quality supervision. We address this by reconceptualizing data synthesis through the lens of real-world software evolution. […]

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Automotive engineering development increasingly relies on heterogeneous 3D data, including finite element (FE) models, body-in-white (BiW) representations, CAD geometry, and CFD meshes. At the same time, engineering teams face growing pressure to shorten development cycles, improve performance and accelerate innovation. Although artificial intelligence (AI) is increasingly explored in this domain, many current methods remain task-specific, difficult to interpret, and hard to reuse across development stages. This paper presents a practical graph learning framework for 3D engineering AI, in […]

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· AI does not just change output quality. It changes what humans practice every day. · Research already suggests pressure on verification, confidence calibration, learning transfer, persuasion resistance, and originality. · The next defensible product category is not “more copilot.” It is software that measures and strengthens human cognition around AI workflows.

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Learn how managers use ChatGPT to prepare for conversations, write clear feedback, stay organized, and improve team effectiveness.

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arXiv:2602.09121v1 Announce Type: new Abstract: In this work, we present a lightweight and privacy-preserving Multimodal Emotion Recognition (MER) framework designed for deployment on edge devices. To demonstrate framework’s versatility, our implementation uses three modalities – speech, text and facial imagery. However, the system is fully modular, and can be extended to support other modalities or tasks. Each modality is processed through a dedicated backbone optimized for inference efficiency: Emotion2Vec for speech, a ResNet-based model for facial expressions, and […]

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arXiv:2512.17340v2 Announce Type: replace-cross Abstract: Fair regression methods have the potential to mitigate societal bias concerns in health care, but there has been little work on penalized fair regression when multiple groups experience such bias. We propose a general regression framework that addresses this gap with unfairness penalties for multiple groups. Our approach is demonstrated for binary outcomes with true positive rate disparity penalties. It can be efficiently implemented through reduction to a cost-sensitive classification problem. We additionally […]

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