arXiv:2602.13213v1 Announce Type: new Abstract: Commercial insurance underwriting is a labor-intensive process that requires manual review of extensive documentation to assess risk and determine policy pricing. While AI offers substantial efficiency improvements, existing solutions lack comprehensive reasoning capabilities and internal mechanisms to ensure reliability within regulated, high-stakes environments. Full automation remains impractical and inadvisable in scenarios where human judgment and accountability are critical. This study presents a decision-negative, human-in-the-loop agentic system that incorporates an adversarial self-critique mechanism as […]

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arXiv:2603.10050v1 Announce Type: new Abstract: This paper presents a geometrically explicit formulation for Cosserat rods that unifies configuration-space and strain-based representations within a single modeling framework. The proposed method uses nodal configurations on the Lie group SE(3) as generalized coordinates, while internal strains are reconstructed via a piecewise-linear parameterization. This hybrid design preserves the geometric rigor of Lie-group formulations and retains the locality, simplicity, and computational efficiency characteristic of strain-parameterized rod models. The formulation naturally avoids shear and […]

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arXiv:2602.04233v1 Announce Type: new Abstract: Pre-trained models have become indispensable for efficiently building models across a broad spectrum of downstream tasks. The advantages of pre-trained models have been highlighted by empirical studies on scaling laws, which demonstrate that larger pre-trained models can significantly reduce the sample complexity of downstream learning. However, existing theoretical investigations of pre-trained models lack the capability to explain this phenomenon. In this paper, we provide a theoretical investigation by introducing a novel framework, caulking, […]

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arXiv:2603.22128v2 Announce Type: replace-cross Abstract: While both classical and neural network classifiers can achieve high accuracy, they fall short on offering uncertainty bounds on their predictions, making them unfit for safety-critical applications. Existing kernel-based classifiers that provide such bounds scale with $mathcal O (n^{sim3})$ in time, making them computationally intractable for large datasets. To address this, we propose a novel, computationally efficient classification algorithm based on the Nadaraya-Watson estimator, for whose estimates we derive frequentist uncertainty intervals. We […]

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arXiv:2604.13203v1 Announce Type: new Abstract: Mild Cognitive Impairment (MCI) affects 15-20% of adults aged 65 and older, often making kitchen navigation and independent living difficult, particularly in lower-income communities with limited access to professional design help. This study created an AI system that converts standard kitchen photos into MCI-friendly designs using the Home Design Guidelines (HDG). Stable Diffusion models, enhanced with DreamBooth LoRA and ControlNet, were trained on 100 kitchen images to produce realistic visualizations with open layouts, […]

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I need to OCR 50 million pages of legal documents. I’m only interested in the text, layout is not very important. What is the most cost effective way on how I could tackle this while it not taking longer than 1 week? submitted by /u/vroemboem [link] [comments]

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Claude Code is my daily driver for coding. It’s an agentic CLI tool from Anthropic that reads your codebase, edits files, runs commands — and it’s genuinely good at it. But it has one limitation that kept bothering me: it only talks to one API backend at a time. That’s fine until Anthropic is rate-limited. Or down. Or you want to try a cheaper provider for routine tasks. Or you want teammates on a different backend than your […]

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