technocracy

arXiv:2603.06600v1 Announce Type: new Abstract: Vision Language Models (VLMs) are prone to errors, and identifying where these errors occur is critical for ensuring the reliability and safety of AI systems. In this paper, we propose an approach that automatically generates questions designed to deliberately induce incorrect responses from VLMs, thereby revealing their vulnerabilities. The core of this approach lies in fuzz testing and reinforcement finetuning: we transform a single input query into a large set of diverse variants […]

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You already sense it. The headlines talk about crypto rallies and AI breakthroughs, and part of you knows this is bigger than just news, it’s a shift that should include you. But the path feels lonely. Savings shrink, banks offer nothing, borders slow everything, and the tools that matter seem reserved for someone else. You want to participate, to build, to rise, but you want to do it with people who get it, who lift you as you […]

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Open-set learning and discovery (OSLD) is a challenging machine learning task in which samples from new (unknown) classes can appear at test time. It can be seen as a generalization of zero-shot learning, where the new classes are not known a priori, hence involving the active discovery of new classes. While zero-shot learning has been extensively studied in text classification, especially with the emergence of pre-trained language models, open-set learning and discovery is a comparatively new setup for […]

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Wildfire spread prediction demands hyper-local accuracy at scales unattainable by traditional physics-based models or coarse satellite observations. This paper introduces a novel Digital Twin AI framework leveraging 5G IoT mesh networks to deliver real-time, 10m×10m resolution fire propagation forecasts with 5-60-minute lead times. Deployed across 1,200 self-healing sensor nodes, the system fuses multi-modal environmental data thermal anomalies, 3D winds profiles, dynamic fuel moisture at 100Hz through graph attention networks, feeding physics-informed neural twins synchronized via unscented Kalman filtering. […]

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arXiv:2601.12205v1 Announce Type: new Abstract: This paper investigates three crucial yet underexplored aspects of the generalization capabilities of neural audio codecs (NACs): (i) whether NACs can generalize to unseen languages during pre-training, (ii) whether speech-only pre-trained NACs can effectively generalize to non-speech applications such as environmental sounds, music, and animal vocalizations, and (iii) whether incorporating non-speech data during pre-training can improve performance on both speech and non-speech tasks. Existing studies typically rely on off-the-shelf NACs for comparison, which […]

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Time series machine learning (TSML) is a growing research field that spans a wide range of tasks. The popularity of established tasks such as classification, clustering, and extrinsic regression has, in part, been driven by the availability of benchmark datasets. An archive of 30 multivariate time series classification datasets, introduced in 2018 and commonly known as the UEA archive, has since become an essential resource cited in hundreds of publications. We present a substantial expansion of this archive […]

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In this tutorial, we explore NadirClaw as an intelligent routing layer that classifies prompts into simple and complex tiers before sending them to the most suitable model. We start by installing the required packages, setting up an optional Gemini API key, and testing the local classifier through the NadirClaw CLI without making any live LLM calls. We then inspect the centroid vectors that power the routing decision, embed our own prompts, visualize how similarity scores separate simple and […]

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is a philosophical essay by Aldous Huxley in which he discusses the relationship between bright, colorful objects, geometric designs, psychoactives, art, and profound experience. Heaven and Hell metaphorically refer to what Huxley conceives to be two contrary mystical experiences that potentially await when one opens the “doors of perception”—not only in a mystical experience, but in prosaic life.

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arXiv:2601.02447v1 Announce Type: new Abstract: Routine clinical imaging of the retina using optical coherence tomography (OCT) is performed with large slice spacing, resulting in highly anisotropic images and a sparsely scanned retina. Most learning-based methods circumvent the problems arising from the anisotropy by using 2D approaches rather than performing volumetric analyses. These approaches inherently bear the risk of generating inconsistent results for neighboring B-scans. For example, 2D retinal layer segmentations can have irregular surfaces in 3D. Furthermore, the […]

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