Tallinn, Estonia, March 4th, 2026/Chainwire/–CryptoJets, a global private jet and helicopter brokerage, has announced a partnership with Transacta to support the growing demand for cryptocurrency payments in private aviation.  The growing demand for fast and secure crypto payments Demand for cryptocurrency payment options in luxury travel continues to grow as wealth shifts toward younger generations. The private aviation sector is increasingly embracing digital currencies, driven by both practical needs and broader market development. Built for travelers who value […]

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Smartphone sensing offers an unobtrusive and scalable way to track daily behaviors linked to mental health, capturing changes in sleep, mobility, and phone use that often precede symptoms of stress, anxiety, or depression. While most prior studies focus on detection that responds to existing conditions, forecasting mental health enables proactive support through Just-in-Time Adaptive Interventions. In this paper, we present the first comprehensive benchmarking study comparing traditional machine learning (ML), deep learning (DL), and large language model (LLM) […]

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The uncomfortable truth: “model choice” is half your prompt engineering If your prompt is a recipe, the model is your kitchen. A great recipe doesn’t help if: the oven is tiny (context window), the ingredients are expensive (token price), the chef is slow (latency), or your tools don’t fit (function calling / JSON / SDK / ecosystem). So here’s a practical comparison you can actually use. Note on “parameters”: for many frontier models, parameter counts are not publicly […]

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arXiv:2602.20291v1 Announce Type: new Abstract: Data visualizations are central to scientific communication, journalism, and everyday decision-making, yet they are frequently prone to errors that can distort interpretation or mislead audiences. Rule-based visualization linters can flag violations, but they miss context and do not suggest meaningful design changes. Directly querying general-purpose LLMs about visualization quality is unreliable: lacking training to follow visualization design principles, they often produce inconsistent or incorrect feedback. In this work, we introduce a framework that […]

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arXiv:2602.02568v1 Announce Type: new Abstract: We introduce $textbf{Hierarchical Taylor Series-based Continual Learning (HTCL)}$, a framework that couples fast local adaptation with conservative, second-order global consolidation to address the high variance introduced by random task ordering. To address task-order effects, HTCL identifies the best intra-group task sequence and integrates the resulting local updates through a Hessian-regularized Taylor expansion, yielding a consolidation step with theoretical guarantees. The approach naturally extends to an $L$-level hierarchy, enabling multiscale knowledge integration in a […]

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Every SaaS IT director knows the drill. Engineering is rushing a release to hit a revenue goal, a dozen new sales reps are demanding CRM access yesterday, and then a critical vulnerability notification lands in your inbox. Speed usually wins when the pressure is on, so you grant admin rights to unblock a developer or pause a fleet-wide update to keep a sales demo running smooth. These all feel like tactical necessities in the moment, but they create […]

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The deployment of autonomous AI agents—systems capable of using tools and executing code—presents a unique security challenge. While standard LLM applications are restricted to text-based interactions, autonomous agents require access to shell environments, file systems, and network endpoints to perform tasks. This increased capability introduces significant risks, as a model’s ‘black box’ nature can lead to unintended command execution or unauthorized data access. NVIDIA has addressed this gap by open-sourcing OpenShell, a dedicated runtime environment designed to facilitate […]

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Contrastive Forward-Forward (CFF) learning trains Vision Transformers layer by layer against supervised contrastive objectives. CFF training can be sensitive to random seed, but the sources of this instability are poorly understood. We focus on one implementation detail: the positive-pair margin in the contrastive loss is applied through saturating similarity clamping, $min(s + m,, 1)$. We prove that an alternative formulation, subtracting the margin after the log-probability, is gradient-neutral under the mean-over-positives reduction. On CIFAR-10 ($2 times 2$ factorial, […]

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arXiv:2601.12164v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed as analytical tools across multilingual contexts, yet their outputs may carry systematic biases conditioned by the language of the prompt. This study presents an experimental comparison of LLM-generated political analyses of a Ukrainian civil society document, using semantically equivalent prompts in Russian and Ukrainian. Despite identical source material and parallel query structures, the resulting analyses varied substantially in rhetorical positioning, ideological orientation, and interpretive conclusions. The […]

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