:::info Astounding Stories of Super-Science July, 2008, by Astounding Stories is part of HackerNoon’s Book Blog Post series. You can jump to any chapter in this book here. The Call of the Wild – The Dominant Primordial Beast Astounding Stories of Super-Science July 2008: The Call of the Wild – The Dominant Primordial Beast By Jack London ::: The dominant primordial beast was strong in Buck, and under the fierce conditions of trail life it grew and grew. Yet […]

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We study offline learning in KL-regularized two-player zero-sum games, where policies are optimized under a KL constraint to a fixed reference policy. Prior work relies on pessimistic value estimation to handle distribution shift, yielding only $widetilde{mathcal{O}}(1/sqrt n)$ statistical rates. We develop a new pessimism-free algorithm and analytical framework for KL-regularized games, built on the smoothness of KL-regularized best responses and a stability property of the Nash equilibrium induced by skew symmetry. This yields the first $widetilde{mathcal{O}}(1/n)$ sample complexity […]

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arXiv:2603.03315v1 Announce Type: new Abstract: Internet memes are a powerful form of online communication, yet their nature and reliance on commonsense knowledge make toxicity detection challenging. Identifying key features for meme interpretation and understanding, is a crucial task. Previous work has been focused on some elements contributing to the meaning, such as the Textual dimension via OCR, the Visual dimension via object recognition, upper layers of meaning like the Emotional dimension, Toxicity detection via proxy variables, such as […]

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datasette PR #2689: Replace token-based CSRF with Sec-Fetch-Site header protection Datasette has long protected against CSRF attacks using CSRF tokens, implemented using my asgi-csrf Python library. These are something of a pain to work with – you need to scatter forms in templates with <input type=”hidden” name=”csrftoken” value=”{{ csrftoken() }}”> lines and then selectively disable CSRF protection for APIs that are intended to be called from outside the browser. I’ve been following Filippo Valsorda’s research here with interest, […]

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The software engineering industry relies heavily on Large Language Models (LLMs) accessed via flat-rate web subscriptions, operating under the assumption of infinite, highly available compute. However, the paradigm of “unlimited compute” is fundamentally incompatible with the physical constraints of data centers. Surging enterprise demand and massive context windows have forced AI providers to introduce peak-hour usage rationing. In metropolitan hubs, the concentration of demand leads to significant rate-limiting during standard business hours (08:00–14:00 EST). In empirical observations of […]

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arXiv:2601.06730v2 Announce Type: replace-cross Abstract: The Rashomon effect — the existence of multiple, distinct models that achieve nearly equivalent predictive performance — has emerged as a fundamental phenomenon in modern machine learning and statistics. In this paper, we explore the causes underlying the Rashomon effect, organizing them into three categories: statistical sources arising from finite samples and noise in the data-generating process; structural sources arising from non-convexity of optimization objectives and unobserved variables that create fundamental non-identifiability; and […]

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arXiv:2604.13242v1 Announce Type: new Abstract: Large language models (LLMs), particularly when integrated into agentic systems, have demonstrated human- and even superhuman-level performance across multiple domains. Whether these systems can truly be considered creative, however, remains a matter of debate, as conclusions heavily depend on the definitions, evaluation methods, and specific use cases employed. In this paper, we analyse creativity along two complementary macro-level perspectives. The first is a functionalist perspective, focusing on the observable characteristics of creative outputs. […]

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arXiv:2601.09730v1 Announce Type: new Abstract: Clinical document metadata, such as document type, structure, author role, medical specialty, and encounter setting, is essential for accurate interpretation of information captured in clinical documents. However, vast documentation heterogeneity and drift over time challenge harmonization of document metadata. Automated extraction methods have emerged to coalesce metadata from disparate practices into target schema. This scoping review aims to catalog research on clinical document metadata extraction, identify methodological trends and applications, and highlight gaps. […]

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arXiv:2604.20492v1 Announce Type: new Abstract: In this paper, it is shown, for the first time, that centralized performance is achievable in decentralized learning without sharing the local datasets. Specifically, when clients adopt an empirical risk minimization with relative-entropy regularization (ERM-RER) learning framework and a forward-backward communication between clients is established, it suffices to share the locally obtained Gibbs measures to achieve the same performance as that of a centralized ERM-RER with access to all the datasets. The core […]

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