arXiv:2509.11070v3 Announce Type: replace Abstract: We develop a stochastic approximation framework for learning nonlinear operators between infinite-dimensional spaces utilizing general Mercer operator-valued kernels. Our framework encompasses two key classes: (i) compact kernels, which admit discrete spectral decompositions, and (ii) diagonal kernels of the form $K(x,x’)=k(x,x’)T$, where $k$ is a scalar-valued kernel and $T$ is a positive operator on the output space. This broad setting induces expressive vector-valued reproducing kernel Hilbert spaces (RKHSs) that generalize the classical $K=kI$ paradigm, […]

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In the current study, we propose a novel reduced-order model for the drag coefficient of a circular cylinder model that can be either fixed or undergoing an oscillatory linear motion in the cross-flow direction, the streamwise direction, or at an arbitrary tilt angle. Thus, the proposed model is not restricted to a single geometric setting of the cylinder. The model establishes a proper nonlinear coupling between the drag coefficient and the lift coefficient, such that the drag coefficient […]

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How Governance Turns AI Agent Experiments into Predictable Business Outcomes 1. The Strategic Inflection Point: From AI Tools to an Autonomous Workforce As we approach 2026, enterprises worldwide face a pivotal moment – the dawn of the “agent era.” Companies are swiftly transitioning from basic generative AI applications to sophisticated autonomous systems capable of handling complex business tasks. This evolution has unveiled a critical challenge: the “paradox of increasing investment versus uncertain returns.” Despite widespread adoption, with 79% […]

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arXiv:2601.08857v1 Announce Type: new Abstract: The integration of Large Language Models (LLMs), such as ChatGPT and GitHub Copilot, into professional workflows is increasingly reshaping software engineering practices. These tools have lowered the cost of code generation, explanation, and testing, while introducing new forms of automation into routine development tasks. In contrast, most of the software engineering and computer engineering curricula remain closely aligned with pedagogical models that equate manual syntax production with technical competence. This growing misalignment raises […]

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Remote work managers are extremely stressed in 2026… but why? As the keepers of productivity, it’s important to know the team is working but not feeling watched. Monitoring software, screenshots every ten minutes, activity percentages, app usage logs, have been the answer in the past. What happened? Employees felt surveilled, trust eroded quietly, and workplace dynamics became parole, not a partnership. But, what if all that tracking data was ineffective? How Do People Really Work? Time tracking has […]

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arXiv:2506.12227v2 Announce Type: replace-cross Abstract: Ensuring fairness in machine learning requires understanding how sensitive attributes like race or gender causally influence outcomes. Existing causal discovery (CD) methods often struggle to recover fairness-relevant pathways in the presence of noise, confounding, or data corruption. Large language models (LLMs) offer a complementary signal by leveraging semantic priors from variable metadata. We propose a hybrid LLM-guided CD framework that extends a breadth-first search strategy with active learning and dynamic scoring. Variable pairs […]

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