technocracy

arXiv:2601.08837v1 Announce Type: new Abstract: Safety mechanisms in LLMs remain vulnerable to attacks that reframe harmful requests through culturally coded structures. We introduce Adversarial Tales, a jailbreak technique that embeds harmful content within cyberpunk narratives and prompts models to perform functional analysis inspired by Vladimir Propp’s morphology of folktales. By casting the task as structural decomposition, the attack induces models to reconstruct harmful procedures as legitimate narrative interpretation. Across 26 frontier models from nine providers, we observe an […]

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arXiv:2411.09847v3 Announce Type: replace-cross Abstract: There has been a recent critical need to study fairness and bias in machine learning (ML) algorithms. Since there is clearly no one-size-fits-all solution to fairness, ML methods should be developed alongside bias mitigation strategies that are practical and approachable to the practitioner. Motivated by recent work on “fair” PCA, here we consider the more challenging method of non-negative matrix factorization (NMF) as both a showcasing example and a method that is important […]

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This work constructs, analyzes and simulates a modified SIR epidemiological model for the spread of a generic long-time disease, in which the coefficients of infectivity and death rate are system variables. Diseases, such as COVID-19, have demonstrated very clearly that infectivity and death rates can change over time, even for the same variant of the virus, due to vaccination, improved treatments, better analysis, better medications, etc. This motivates us to model a generic disease where the infectivity and […]

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arXiv:2601.14461v1 Announce Type: new Abstract: The Fokker-Planck (FP) particle method accelerates rarefied-gas simulations by replacing the binary collisions of the commonly used Direct Simulation Monte Carlo (DSMC) method with a drift=diffusion process. Like all particle methods, the FP method is inherently stochastic, which leads to statistical fluctuations in macroscopic quantities and necessitates large particle numbers for accurate results. In this work, we investigate the use of quasi-random numbers, which sample distributions more evenly and thereby reduce the variance. […]

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arXiv:2602.02600v1 Announce Type: new Abstract: Diffusion language models (DLMs) have recently emerged as a promising alternative to autoregressive (AR) models, offering parallel decoding and controllable sampling dynamics while achieving competitive generation quality at scale. Despite this progress, the role of sampling mechanisms in shaping refusal behavior and jailbreak robustness remains poorly understood. In this work, we present a fundamental analytical framework for step-wise refusal dynamics, enabling comparison between AR and diffusion sampling. Our analysis reveals that the sampling […]

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For years, discovering YouTube creators from other parts of the world came with a familiar trade-off. You either accepted awkward subtitles or skipped the video altogether. Language has always been the barrier that decides what content travels and what stays local. YouTube has been trying to remove those barriers for a while now. To give you an example, YouTube started with basic captions, then came auto-translation, and eventually AI-powered voice dubbing. Today, the video streaming giant has announced […]

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Exito Media Concepts today announced the 46th Edition of the Digital Transformation Summit Malaysia 2026, convening on 23rd April 2026 at the Hyatt Regency Kuala Lumpur Midtown. The summit will bring together more than 200 C-Level executives, technology directors, and government leaders to address the strategic and operational imperatives shaping Malaysia’s next phase of digital growth. Now in its 46th edition and part of a series spanning 11+ countries, the summit has established itself as one of Asia […]

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arXiv:2604.14975v1 Announce Type: cross Abstract: To obtain more accurate model parameters and improve prediction accuracy, we proposed a regularized Kriging model that penalizes the hyperparameter theta in the Gaussian stochastic process, termed the Theta-regularized Kriging. We derived the optimization problem for this model from a maximum likelihood perspective. Additionally, we presented specific implementation details for the iterative process, including the regularized optimization algorithm and the geometric search cross-validation tuning algorithm. Three distinct penalty methods, Lasso, Ridge, and Elastic-net […]

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