Modern iOS applications that have evolved over multiple years often accumulate complex UI layers, deeply integrated analytics, and carefully tuned performance optimizations. These applications typically rely on UIKit as the foundational framework, making any transition to SwiftUI a non-trivial architectural decision. SwiftUI introduces a declarative paradigm that improves developer productivity and maintainability, but migrating large production systems requires careful orchestration to avoid regressions in performance, reliability, and user experience. A structured migration approach becomes essential when the goal […]

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arXiv:2602.12471v1 Announce Type: new Abstract: We consider the optimization problem of minimizing the logistic loss with gradient descent to train a linear model for binary classification with separable data. With a budget of $T$ iterations, it was recently shown that an accelerated $1/T^2$ rate is possible by choosing a large step size $eta = Theta(gamma^2 T)$ (where $gamma$ is the dataset’s margin) despite the resulting non-monotonicity of the loss. In this paper, we provide a tighter analysis of […]

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Over the last 12 months, I’ve built a model to predict the next token and to suggest synonyms or related queries to a user prompt, with 100% correct predictions on the training set in one shot, without training or deep neural networks (DNNs). The same model is now integrated in some of the most recent LLM architectures, albeit with costly training via DNNs. My version does not need DNNs or training. The purpose of this article is to […]

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arXiv:2602.20202v1 Announce Type: new Abstract: The growing reliance on AI-identified digital evidence raises significant concerns about its reliability, particularly as large language models (LLMs) are increasingly integrated into forensic investigations. This paper proposes a structured framework that automates forensic artifact extraction, refines data through LLM-driven analysis, and validates results using a Digital Forensic Knowledge Graph (DFKG). Evaluated on a 13 GB forensic image dataset containing 61 applications, 2,864 databases, and 5,870 tables, the framework ensures artifact traceability and […]

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arXiv:2603.10033v1 Announce Type: new Abstract: Graph foundation models (GFM) aim to acquire transferable knowledge by pre-training on diverse graphs, which can be adapted to various downstream tasks. However, domain shift in graphs is inherently two-dimensional: graphs differ not only in what they describe (topic domains) but also in how they are represented (format domains). Most existing GFM benchmarks vary only topic domains, thereby obscuring how knowledge transfers across both dimensions. We present a new benchmark that jointly evaluates […]

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arXiv:2604.02488v1 Announce Type: new Abstract: Time-series causal discovery methods rely on assumptions such as stationarity, regular sampling, and bounded temporal dependence. When these assumptions are violated, structure learning can produce confident but misleading causal graphs without warning. We introduce Causal-Audit, a framework that formalizes assumption validation as calibrated risk assessment. The framework computes effect-size diagnostics across five assumption families (stationarity, irregularity, persistence, nonlinearity, and confounding proxies), aggregates them into four calibrated risk scores with uncertainty intervals, and applies […]

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When Netflix launched its ad-supported tier in November 2022, most people shrugged it off as a pricing accommodation for budget-conscious subscribers. It was a bigger deal than it looked. The AVOD vs SVOD debate had been a niche industry conversation up to that point. Netflix, a company that spent a decade telling advertisers they had no place in its product, had just admitted its model needed a second revenue engine. Every major platform followed, and the structural logic […]

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arXiv:2602.06194v1 Announce Type: new Abstract: Asynchronous, text-based discourse-such as students’ posts in discussion forums-is widely used to support collaborative learning. However, the distributed and evolving nature of such discourse often makes it difficult to see how ideas connect, develop, and build on one another over time. As a result, learners may struggle to recognize relationships among ideas-a process that is critical for idea advancement in productive collaborative discourse. To address this challenge, we explore how large language models […]

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