Autonomous agents are increasingly deployed in both offensive and defensive cyber operations, creating high-speed, closed-loop interactions in critical infrastructure environments. Advanced Persistent Threat (APT) actors exploit "Living off the Land" techniques and targeted telemetry perturbations to induce ambiguity in monitoring systems, causing automated defenses to overreact or misclassify benign behavior as malicious activity. Existing monolithic and multi-agent defense pipelines largely operate on correlation-based signals, lack structural constraints on response actions, and are vulnerable to reasoning drift under ambiguous […]

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Venture capital (VC) investments in early-stage startups that end up being successful can yield high returns. However, predicting early-stage startup success remains challenging due to data scarcity (e.g., many VC firms have information about only a few dozen of early-stage startups and whether they were successful). This limits the effectiveness of traditional machine learning methods that rely on large labeled datasets for model training. To address this challenge, we propose an in-context learning framework for startup success prediction […]

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This article describes a hypothetical but realistic scenario, synthesized from patterns commonly observed in large enterprise and retail BI environments. While inspired by real-world experiences, the situation, figures, and timelines have been intentionally anonymized and generalized. Our team owned the data. We owned the ingestion pipelines. We owned the transformations. We owned the semantic model that powered enterprise reporting. The dashboard, however, was built by another team – on top of the model we maintained. That’s how self-serve […]

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Anthropic’s Claude models will now be present in Microsoft Foundry, hosted on Microsoft Azure and boosted by Nvidia GB300 Blackwell Ultra GPUs. The access depicts a change in how proprietary AI is being embedded and sold. Instead of providing a standalone model, Microsoft, Nvidia, and Anthropic put forward a clustered stack that combines model access, architecture, regulation, and adoption. For Azure-focused organizations building sovereign and domain-specific agents, the focus is no longer model quality but efficiency, control, and […]

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Finding a cleaner path through job applications Job searching has become its own kind of workload. A candidate may update a CV in the morning, return to a cover letter after lunch, save a role for later, and then realize the tracking notes live somewhere else entirely. The search can start to feel crowded before the hiring process has even begun. JobAssist.com is built around that everyday pressure.  The platform brings application materials, AI-assisted writing support, and job […]

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arXiv:2601.12305v1 Announce Type: new Abstract: We propose a novel MLaaS Dataset Generator (MDG) framework that creates configurable and reproducible datasets for evaluating Machine Learning as a Service (MLaaS) selection and composition. MDG simulates realistic MLaaS behaviour by training and evaluating diverse model families across multiple real-world datasets and data distribution settings. It records detailed functional attributes, quality of service metrics, and composition-specific indicators, enabling systematic analysis of service performance and cross-service behaviour. Using MDG, we generate more than […]

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arXiv:2603.02607v1 Announce Type: new Abstract: Sparse PCA is one of the most well-studied problems in high-dimensional statistics. In this problem, we are given samples from a distribution with covariance $Sigma$, whose top eigenvector $v in R^d$ is $s$-sparse. Existing sparse PCA algorithms can be broadly categorized into (1) combinatorial algorithms (e.g., diagonal or elementwise covariance thresholding) and (2) SDP-based algorithms. While combinatorial algorithms are much simpler, they are typically only analyzed under the spiked identity model (where $Sigma […]

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arXiv:2603.05719v1 Announce Type: new Abstract: Training machine learning models for radioisotope identification using gamma spectroscopy remains an elusive challenge for many practical applications, largely stemming from the difficulty of acquiring and labeling large, diverse experimental datasets. Simulations can mitigate this challenge, but the accuracy of models trained on simulated data can deteriorate substantially when deployed to an out-of-distribution operational environment. In this study, we demonstrate that unsupervised domain adaptation (UDA) can improve the ability of a model trained […]

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arXiv:2603.15639v1 Announce Type: new Abstract: AI agents are increasingly granted economic agency (executing trades, managing budgets, negotiating contracts, and spawning sub-agents), yet current frameworks gate this agency on capability benchmarks that are empirically uncorrelated with operational robustness. We introduce the Comprehension-Gated Agent Economy (CGAE), a formal architecture in which an agent’s economic permissions are upper-bounded by a verified comprehension function derived from adversarial robustness audits. The gating mechanism operates over three orthogonal robustness dimensions: constraint compliance (measured by […]

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arXiv:2602.09246v1 Announce Type: new Abstract: The integration of Artificial Intelligence (AI) into Higher Education Institutions (HEIs) in Ecuador is not a technological option but a strategic imperative to prevent institutional obsolescence and academic irrelevance in Latin America. This paper presents the IA593 Framework, a governance, ethics, and operational model designed for the Universidad Nacional de Loja (UNL) and scalable as a reference for the Ecuadorian higher education system. The current context reveals a critical urgency: the Latin American […]

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