Metal additive manufacturing (AM) enables the fabrication of safety-critical components, but reliable quality assurance depends on high-fidelity sensor streams containing proprietary process information, limiting collaborative data sharing. Existing defect-detection models typically treat melt-pool observations as independent samples, ignoring layer-wise physical couplings. Moreover, conventional privacy-preserving techniques, particularly Local Differential Privacy (LDP), lead to severe utility degradation because they inject uniform noise across all feature dimensions. To address these interrelated challenges, we propose FI-LDP-HGAT. This computational framework combines two methodological […]

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arXiv:2602.12381v1 Announce Type: new Abstract: Recent generative models produce near-photorealistic images, challenging the trustworthiness of photographs. Synthetic image detection (SID) has thus become an important area of research. Prior work has highlighted how synthetic images differ from real photographs–unfortunately, SID methods often struggle to generalize to novel generative models and often perform poorly in practical settings. CLIP, a foundational vision-language model which yields semantically rich image-text embeddings, shows strong accuracy and generalization for SID. Yet, the underlying relevant […]

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Introduction: The Evolution of Distributed Architecture In the early days of Java development, we relied on the “Cargo Ship” architecture: massive, monolithic deployments that handled every request within a single, unified codebase. While this simplified transactions, it created a fragile ecosystem where one memory leak could crash the entire platform. For a modern Java architect, the challenge is no longer just writing logic; it is designing systems that survive the inherent instability of distributed networks. This guide explores […]

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Anthropic’s April 2026 release of Claude Mythos Preview, and the subsequent emergence of “Mythos-class” as a descriptor for frontier autonomous offensive cyber capability, has prompted institutional response across financial regulation, but no blockchain-specific analytical or policy framework. This paper develops one. We define Mythos-class as a vendor-neutral capability profile comprising five primitives — autonomous discovery at codebase scale, multi-step exploit chaining, agentic execution with tool use, sub-day weaponization, and generality across target classes — and we engage the […]

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This is an overview of the LLM development process. This one-hour talk focuses on the essential three stages of developing an LLM: coding the architecture, implementing pretraining, and fine-tuning the LLM. Lastly, we also discuss the main ways LLMs are evaluated, along with the caveats of each method.

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arXiv:2602.03168v1 Announce Type: new Abstract: Online conformal prediction (OCP) seeks prediction intervals that achieve long-run $1-alpha$ coverage for arbitrary (possibly adversarial) data streams, while remaining as informative as possible. Existing OCP methods often require manual learning-rate tuning to work well, and may also require algorithm-specific analyses. Here, we develop a general regret-to-coverage theory for interval-valued OCP based on the $(1-alpha)$-pinball loss. Our first contribution is to identify emph{linearized regret} as a key notion, showing that controlling it implies […]

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arXiv:2604.08558v1 Announce Type: new Abstract: Recent decoder-only autoregressive text-to-speech (AR-TTS) models produce high-fidelity speech, but their memory and compute costs scale quadratically with sequence length due to full self-attention. In this paper, we propose WAND, Windowed Attention and Knowledge Distillation, a framework that adapts pretrained AR-TTS models to operate with constant computational and memory complexity. WAND separates the attention mechanism into two: persistent global attention over conditioning tokens and local sliding-window attention over generated tokens. To stabilize fine-tuning, […]

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Fraudulent activities on digital banking services are becoming more intricate by the day, challenging existing defenses. While older rule driven methods struggle to keep pace, even precision focused algorithms fall short when new scams are introduced. These tools typically overlook subtle shifts in criminal behavior, missing crucial signals. Because silent breaches cost institutions far more than flagged but legitimate actions, catching every possible case is crucial. High sensitivity to actual threats becomes essential when oversight leads to heavy […]

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