technocracy

arXiv:2604.14163v1 Announce Type: new Abstract: Maritime distress communications transmitted over very high frequency (VHF) radio are safety-critical voice messages used to report emergencies at sea. Under the Global Maritime Distress and Safety System (GMDSS), such messages follow standardized procedures and are expected to convey essential details, including vessel identity, position, nature of the distress, and required assistance. In practice, however, automatic analysis remains difficult because distress messages are often brief, noisy, and produced under stress, may deviate from […]

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The prevalence and low cost of LLMs have led to a rise of synthetic content. From review sites to court documents, “natural” content has been contaminated by data points that appear similar to natural data, but are in fact LLM-generated. In this work we revisit fundamental learning theory questions in this, now ubiquitous, setting. We model this scenario as a sequence of learning tasks where the input is a mix of natural and synthetic data, and the learning […]

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A simple, experience-driven breakdown of context windows, tokens, retrieval, hallucination, and why AI behaves the way it does. THE PROBLEM Three weeks into learning machine learning, I ran into a problem. Not with models or math, but with my notes. I had taken the time to write things in my own words, build analogies that made sense to me, and note down questions I wanted to revisit. The problem wasn’t quality. It was structure. My notes were scattered across […]

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arXiv:2604.17020v1 Announce Type: new Abstract: Static benchmarks for harmful content detection face limitations in scalability and diversity, and may also be affected by contamination from web-scale pre-training corpora. To address these issues, we propose a framework for synthesizing harmful content, leveraging persona-guided large language model (LLM) agents. Our approach constructs two-dimensional user personas by integrating demographic identities and topical interests with situational harmful strategies, enabling the simulation of diverse and contextually grounded harmful interactions. We evaluate the framework […]

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arXiv:2601.03315v1 Announce Type: new Abstract: We report a case study of four end-to-end attempts to autonomously generate ML research papers using a pipeline of six LLM agents mapped to stages of the scientific workflow. Of these four, three attempts failed during implementation or evaluation. One completed the pipeline and was accepted to Agents4Science 2025, an experimental inaugural venue that required AI systems as first authors, passing both human and multi-AI review. From these attempts, we document six recurring […]

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The media companies of the world are set adrift in seas of video, audio, pictures, and words. We are still being fed content every day by production studios, broadcast feeds, streaming services, and digital archives. All this content is not valuable because of the raw footage, but because of the metadata used to describe it. The most salient content often lies concealed within an overcrowded repository lacking high-quality metadata. The issue is that outdated procedures of metadata control […]

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It happened once that it was tantamount to spying into a system of an unsuspecting neighborhood. You would visit a few of the servers, check CPU or memory, and decide that nothing was wrong at the end of the day. Those days are long gone. The new environment of a cloud-native system is far more dynamic and unpredictable. Several seconds are spent before containers move in and out. Inter-cloud transversal microservices clusters. The virtual networks have a journey […]

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arXiv:2508.14936v2 Announce Type: replace-cross Abstract: Synthetic data holds substantial potential to address practical challenges in epidemiology due to restricted data access and privacy concerns. However, many current methods suffer from limited quality, high computational demands, and complexity for non-experts. Furthermore, common evaluation strategies for synthetic data often fail to directly reflect statistical utility and measure privacy risks sufficiently. Against this background, a critical underexplored question is whether synthetic data can reliably reproduce key findings from epidemiological research while […]

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arXiv:2601.22345v1 Announce Type: new Abstract: We evaluate language models on their ability to explore interactive environments under a limited interaction budget. We introduce three parametric tasks with controllable exploration difficulty, spanning continuous and discrete environments. Across state-of-the-art models, we find systematic under-exploration and suboptimal solutions, with performance often significantly worse than simple explore–exploit heuristic baselines and scaling weakly as the budget increases. Finally, we study two lightweight interventions: splitting a fixed budget into parallel executions, which surprisingly improves […]

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arXiv:2601.10768v1 Announce Type: new Abstract: This paper investigates complex product-innovation processes using models grounded in a set of heuristics. Each heuristic is expressed through simple trends — increasing, decreasing, or constant — which serve as minimally information-intensive quantifiers, avoiding reliance on numerical values or rough sets. A solution to a trend model is defined as a set of scenarios with possible transitions between them, represented by a transition graph. Any possible future or past behaviour of the system […]

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