arXiv:2601.14261v1 Announce Type: new Abstract: The intelligent review of power grid engineering design drawings is crucial for power system safety. However, current automated systems struggle with ultra-high-resolution drawings due to high computational demands, information loss, and a lack of holistic semantic understanding for design error identification. This paper proposes a novel three-stage framework for intelligent power grid drawing review, driven by pre-trained Multimodal Large Language Models (MLLMs) through advanced prompt engineering. Mimicking the human expert review process, the […]

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arXiv:2508.20257v2 Announce Type: replace-cross Abstract: The process of discovering equations from data lies at the heart of physics and in many other areas of research, including mathematical ecology and epidemiology. Recently, machine learning methods known as symbolic regression emerged as a way to automate this task. This study presents an overview of the current literature on symbolic regression, while also comparing the efficiency of five state-of-the-art methods in recovering the governing equations from nine processes, including chaotic dynamics […]

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Machine learning models often perform well during experimentation. Offline metrics improve, prototypes demonstrate potential, and early validation builds confidence across teams. In controlled environments, systems behave predictably and progress feels steady. The transition to production introduces a different set of pressures. Training jobs fail intermittently. Features arrive outside expected time windows. Historical data changes without notice. Deployments slow as teams hesitate, unsure of downstream consequences. What worked in isolation begins to strain under operational reality. The cause is […]

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Learning solution operators for systems with complex, varying geometries and parametric physical settings is a central challenge in scientific machine learning. In many-query regimes such as design optimization, control and inverse problems, surrogate modeling must generalize across geometries while allowing flexible evaluation at arbitrary spatial locations. In this work, we propose Arbitrary Geometry-encoded Transformer (ArGEnT), a geometry-aware attention-based architecture for operator learning on arbitrary domains. ArGEnT employs Transformer attention mechanisms to encode geometric information directly from point-cloud representations […]

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I want to talk about something that has been quietly changing how serious developers build with AI tools. It did not happen overnight. There was no single blog post or conference talk that kicked it off. It happened gradually, the way most good ideas in software do. Developers ran into the same problems over and over again, figured out better ways to solve them, and those better ways eventually got names. The names are: prompt engineering, context engineering, and harness engineering. […]

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