arXiv:2312.10618v2 Announce Type: replace-cross Abstract: Classification and probability estimation are fundamental tasks with broad applications across modern machine learning and data science, spanning fields such as biology, medicine, engineering, and computer science. Recent development of weighted Support Vector Machines (wSVMs) has demonstrated considerable promise in robustly and accurately predicting class probabilities and performing classification across a variety of problems (Wang et al., 2008). However, the existing framework relies on an $ell^2$-norm regularized binary wSVMs optimization formulation, which is […]

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arXiv:2603.23613v1 Announce Type: new Abstract: Large Language Models (LLMs) are showing remarkable performance in generating source code, yet the generated code often has issues like compilation errors or incorrect code. Researchers and developers often face wasted effort in implementing checks and refining LLM-generated code, frequently duplicating their efforts. This paper presents LLMLOOP, a framework that automates the refinement of both source code and test cases produced by LLMs. LLMLOOP employs five iterative loops: resolving compilation errors, addressing static […]

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arXiv:2603.24736v1 Announce Type: new Abstract: In the design and safety analysis of advanced reactor systems, constructing input files for system-level thermal-hydraulics codes such as the System Analysis Module (SAM) remains a labor-intensive task. Analysts must extract and reconcile design data from heterogeneous engineering documents and manually translate it into solver-specific syntax. In this paper, we present AutoSAM, an agentic framework that automates SAM input file generation. The framework combines a large language model agent with retrieval-augmented generation over […]

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arXiv:2601.00095v1 Announce Type: new Abstract: Large language models increasingly require structured inference, from JSON schema enforcement to multi-lingual parsing, where outputs must satisfy complex constraints. We introduce MetaJuLS, a meta-reinforcement learning approach that learns universal constraint propagation policies applicable across languages and tasks without task-specific retraining. By formulating structured inference as adaptive constraint propagation and training a Graph Attention Network with meta-learning, MetaJuLS achieves 1.5–2.0$times$ speedups over GPU-optimized baselines while maintaining within 0.2% accuracy of state-of-the-art parsers. On […]

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The allocation of scarce donor organs constitutes one of the most consequential algorithmic challenges in healthcare. While the field is rapidly transitioning from rigid, rule-based systems to machine learning and data-driven optimization, we argue that current approaches often overlook a fundamental barrier: incentives. In this position paper, we highlight that organ allocation is not merely a static optimization problem, but rather a complex game involving transplant centers, clinicians, and regulators. Focusing on US adult heart transplant allocation, we […]

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While climate models provide insights for climate decision-making, their use is constrained by significant computational and technical demands. Although machine learning (ML) emulators offer a way to bypass the high computational costs, their effective use remains challenging. The hurdles are diverse, ranging from limited accessibility and a lack of specialized knowledge to a general mistrust of ML methods that are perceived as insufficiently physical. Here, we introduce a framework to overcome these barriers by integrating both climate science […]

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For years, undervolting has been the holy grail of laptop thermal management. It is the first thing any enthusiast does with a new machine. Fine-tune the voltage curve in MSI Afterburner, run a stress test, and enjoy lower temperatures and potentially higher boost clocks. I have been doing it for every laptop I have owned. But when I started running long, multi-hour Stable Diffusion sessions, I discovered a frustrating truth: undervolting is not enough. It is a fantastic […]

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Release: datasette-ports 0.1 Another example of README-driven development, this time solving a problem that might be unique to me. I often find myself running a bunch of different Datasette instances with different databases and different in-development plugins, spreads across dozens of different terminal windows – enough that I frequently lose them! Now I can run this: datasette install datasette-ports datasette ports And get a list of every running instance that looks something like this: http://127.0.0.1:8333/ – v1.0a26 Databases: […]

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Clark Packard Last week, Disney+ released a restored version of The Beatles Anthology, the landmark 1995 ABC eight-part documentary series that remains the closest thing to a definitive account of the band’s extraordinary journey. I binge-watched the updated Anthology over the Thanksgiving holiday weekend, which brought back memories of watching the original with my parents 30 years ago. Now I’m the parent, though my wife and four-year-old son were less enthusiastic about the marathon viewing sessions. Last year, I […]

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arXiv:2603.02370v1 Announce Type: new Abstract: Large Vision-Language Models (LVLMs) have grown increasingly powerful in recent years, but can also exhibit harmful biases. Prior studies investigating such biases have primarily focused on demographic traits related to the visual characteristics of a person depicted in an image, such as their race or gender. This has left biases related to cultural differences (e.g., religion, socioeconomic status), which cannot be readily discerned from an individual’s appearance alone, relatively understudied. A key challenge […]

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