technocracy

arXiv:2601.03260v1 Announce Type: new Abstract: The rapid development of AI agent has spurred the development of advanced research tools, such as Deep Research. Achieving this require a nuanced understanding of the relations within scientific literature, surpasses the scope of keyword-based or embedding-based retrieval. Existing retrieval agents mainly focus on the content-level similarities and are unable to decode critical relational dynamics, such as identifying corroborating or conflicting studies or tracing technological lineages, all of which are essential for a […]

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arXiv:2602.15037v1 Announce Type: new Abstract: As large language models (LLMs) advance toward expert-level performance in engineering domains, reliable reasoning under user-specified constraints becomes critical. In circuit analysis, for example, a numerically correct solution is insufficient if it violates established methodological conventions such as mesh directionality or polarity assignments, errors that can propagate in safety-critical systems. Yet it remains unclear whether frontier models truly apply first-principles reasoning or rely on entrenched training priors that conflict with explicit instructions. We […]

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Gen Z isn’t “the future” audience anymore. They are the growth engine right now. By 2026, the brands that win with Gen Z won’t just market products—they’ll build communities that shape culture and commerce together. As a CMO working with Gen Z–first brands, I’ve seen a consistent pattern emerge: campaigns can still go viral, but the brands that build fandoms, not funnels, grow more efficiently and stick around longer. For years, marketers treated Gen Z like a puzzle: […]

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arXiv:2602.04940v1 Announce Type: new Abstract: Deep learning has emerged as a transformative tool for the neural surrogate modeling of partial differential equations (PDEs), known as neural PDE solvers. However, scaling these solvers to industrial-scale geometries with over $10^8$ cells remains a fundamental challenge due to the prohibitive memory complexity of processing high-resolution meshes. We present Transolver-3, a new member of the Transolver family as a highly scalable framework designed for high-fidelity physics simulations. To bridge the gap between […]

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Stiff dynamical systems present a challenge for machine-learning reduced-order models (ML-ROMs), as explicit time integration becomes unstable in stiff regimes while implicit integration within learning loops is computationally expensive and often degrades training efficiency. Time reparameterization (TR) offers an alternative by transforming the independent variable so that rapid physical-time transients are spread over a stretched-time coordinate, enabling stable explicit integration on uniformly sampled grids. Although several TR strategies have been proposed, their effect on learnability in ML-ROMs remains […]

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Explainable AI (XAI) promises to provide insight into machine learning models’ decision processes, where one goal is to identify failures such as shortcut learning. This promise relies on the field’s assumption that input features marked as important by an XAI must contain information about the target variable. However, it is unclear whether informativeness is indeed the main driver of importance attribution in practice, or if other data properties such as statistical suppression, novelty at test-time, or high feature […]

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At Amazon’s 2025 re:Invent conference, Amazon Web Services (AWS) announced the Nitro Isolation Engine (NIE), a software module tasked with providing resources to AWS clients while ensuring the security of customer data. AWS also announced the formal verification of the isolation engine’s correctness and security guarantees, using a proof assistant called Isabelle/HOL. As the first formally verified cloud hypervisor, NIE sets a new standard for cloud security. A proof assistant is an automated tool that can help human […]

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arXiv:2602.05961v1 Announce Type: cross Abstract: Sampling from a distribution $p(x) propto e^{-mathcal{E}(x)}$ known up to a normalising constant is an important and challenging problem in statistics. Recent years have seen the rise of a new family of amortised sampling algorithms, commonly referred to as diffusion samplers, that enable fast and efficient sampling from an unnormalised density. Such algorithms have been widely studied for continuous-space sampling tasks; however, their application to problems in discrete space remains largely unexplored. Although […]

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arXiv:2601.02412v1 Announce Type: new Abstract: Recommender systems shape online interactions by matching users with creators content to maximize engagement. Creators, in turn, adapt their content to align with users preferences and enhance their popularity. At the same time, users preferences evolve under the influence of both suggested content from the recommender system and content shared within their social circles. This feedback loop generates a complex interplay between users, creators, and recommender algorithms, which is the key cause of […]

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