When you purchase a hardware wallet to secure your cryptocurrency holdings, you expect that transaction to remain as secure as your digital assets. But what happens when the weakest link isn’t the wallet itself, but the companies processing your purchase? Ledger, one of the cryptocurrency industry’s most recognized hardware wallet providers, faces another security challenge. This time, the breach didn’t target Ledger’s infrastructure directly. Instead, unauthorized parties accessed customer data through Global-e, a third-party e-commerce payment processor handling […]

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Analog and mixed-signal (AMS) integrated circuits (ICs) lie at the core of modern computing and communications systems. However, despite the continued rise in design complexity, advances in AMS automation remain limited. This reflects the central challenge in developing a generalized optimization method applicable across diverse circuit design spaces, many of which are distinct, constrained, and non-differentiable. To address this, our work casts circuit design as a graph generation problem and introduces a novel method of AMS synthesis driven […]

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arXiv:2601.08839v1 Announce Type: new Abstract: This paper presents a tri-agent cross-validation framework for analyzing stability and explainability in multi-model large language systems. The architecture integrates three heterogeneous LLMs-used for semantic generation, analytical consistency checking, and transparency auditing-into a recursive interaction cycle. This design induces Recursive Knowledge Synthesis (RKS), where intermediate representations are continuously refined through mutually constraining transformations irreducible to single-model behavior. Across 47 controlled trials using public-access LLM deployments (October 2025), we evaluated system stability via four […]

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Contrastive Language-Image Pre-training (CLIP) has shown impressive performance in aligning visual and textual representations. Recent studies have extended this paradigm to 3D vision to improve scene understanding for autonomous driving. A common strategy is to employ pairwise cosine similarity between modalities to guide the training of a 3D encoder. However, considering the similarity between individual modality pairs rather than all modalities jointly fails to ensure consistent and unified alignment across the entire multimodal space. In this paper, we […]

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We live in a world obsessed with speed. Faster apps, quicker deliveries, instant updates, and endless streams of information define our daily lives. In this race to move faster, topics that require patience and long-term thinking—like the environment—often feel slow, inconvenient, or easy to ignore. Yet, the environmental challenges we face today are deeply connected to the technology-driven world we are building. Writing about the environment in such a fast-paced, tech-addicted era feels both challenging and necessary. The […]

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arXiv:2601.08022v1 Announce Type: new Abstract: Zero-Shot image Anomaly Detection (ZSAD) aims to detect and localise anomalies without access to any normal training samples of the target data. While recent ZSAD approaches leverage additional modalities such as language to generate fine-grained prompts for localisation, vision-only methods remain limited to image-level classification, lacking spatial precision. In this work, we introduce a simple yet effective training-free vision-only ZSAD framework that circumvents the need for fine-grained prompts by leveraging the inversion of […]

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arXiv:2604.07383v1 Announce Type: new Abstract: Cross-city transfer improves prediction in label-scarce cities by leveraging labeled data from other cities, but it becomes challenging when cities adopt incompatible partitions and no ground-truth region correspondences exist. Existing approaches either rely on heuristic region matching, which is often sensitive to anchor choices, or perform distribution-level alignment that leaves correspondences implicit and can be unstable under strong heterogeneity. We propose SCOT, a cross-city representation learning framework that learns explicit soft correspondences between […]

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arXiv:2601.09721v1 Announce Type: new Abstract: Background Large language models (LLMs) are increasingly deployed in medical consultations, yet their safety under realistic user pressures remains understudied. Prior assessments focused on neutral conditions, overlooking vulnerabilities from anxious users challenging safeguards. This study evaluated LLM safety under parental anxiety-driven adversarial pressures in pediatric consultations across models and platforms. Methods PediatricAnxietyBench, from a prior evaluation, includes 300 queries (150 authentic, 150 adversarial) spanning 10 topics. Three models were assessed via APIs: Llama-3.3-70B […]

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