February 2026

RAG is everywhere—and that’s not surprising. It’s one of the most practical ways to make large document collections queryable without building brittle, domain-specific parsers for every question type. The catch is that what works in a controlled demo often degrades quickly when you put it in front of real enterprise PDFs: scanned contracts, compliance filings, medical records, policies, and the long tail of layout and quality issues that come with them. In production, the “RAG problem” is less […]

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We study the canonical string-based Assembly Index (ASI), defined as the minimum number of binary concatenations needed to construct a target word under full reuse. NP-completeness of ASI-DEC over general finite alphabets and an equivalence between ASI plans and straight-line programs (SLPs) under the same size convention has been established. We emphasize that all transfers between decision variants are effected by explicit polynomial-time mappings and (where needed) an explicit reparameterization of the threshold by an absolute constant or […]

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With the increasing cost of labor, smart agriculture has emerged as a key trend for the future of agricultural development. This paper presents an integrated approach for tomato maturity clas-sification and yield estimation using both RGB and multispectral images. The proposed approach consists of three main components: tomato detection, tomato tracking and counting, and maturity classification of tomatoes. YOLOv8 combined with OSNet is first employed to detect tomatoes, while StrongSORT is then adopted to track consistent identities across […]

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Masked discrete diffusion models (MDMs) are a promising new approach to generative modelling, offering the ability for parallel token generation and therefore greater efficiency than autoregressive counterparts. However, achieving an optimal balance between parallel generation and sample quality remains an open problem. Current approaches primarily address this issue through fixed, heuristic parallel sampling methods. There exist some recent learning based approaches to this problem, but its formulation from the perspective of variational inference remains underexplored. In this work, […]

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In healthcare organizations, ransomware threats are on the rise, resulting in a greater disruption of critical patient care operations, yet current cybersecurity approaches are mostly reactive rather than proactive. As part of this study, a systematic comparative evaluation of traditional machine learning versus deep learning methodologies on small-scale tabular cybersecurity datasets characteristic of healthcare security operations is conducted to address the critical knowledge gap regarding optimal algorithmic approaches for healthcare ransomware threat prediction. In this study, the Healthcare […]

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The resummation of superfactorially divergent series represents a significant computational challenge in mathematical physics. In the present paper the resummation of a specific class of Stieltjes series characterized by a moment sequence growing as $(2n)!$ will be addressed. Despite the fact that Carleman’s condition is satisfied for these series, the practical utility of Padé approximants in resumming them is severely compromised by an extremely slow convergence rate. Weniger’s $delta$-transformation is proposed as a superior resummation tool. Some recently […]

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Let’s be honest: labeling data is the absolute worst part of computer vision. For years, the standard playbook was to scrape millions of images, pay an army of annotators to draw bounding boxes, and feed them into a supervised Convolutional Neural Network. It worked, but it didn’t scale. Then self-supervised learning (SSL) came along, promising a world where models could learn from raw, unlabeled images. But early contrastive methods like SimCLR and MoCo were notoriously heavy, requiring massive batch […]

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