technocracy

In information retrieval (IR), learning-to-rank (LTR) methods have traditionally limited themselves to discriminative machine learning approaches that model the probability of the document being relevant to the query given some feature representation of the query-document pair. In this work, we propose an alternative denoising diffusion-based deep generative approach to LTR that instead models the full joint distribution over feature vectors and relevance labels. While in the discriminative setting, an over-parameterized ranking model may find different ways to fit […]

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arXiv:2510.10968v2 Announce Type: replace-cross Abstract: Derivative-free Bayesian inversion is an important task in many science and engineering applications, particularly when computing the forward model derivative is computationally and practically challenging. In this paper, we introduce Blade, which can produce accurate and well-calibrated posteriors for Bayesian inversion using an ensemble of interacting particles. Blade leverages powerful data-driven priors based on diffusion models, and can handle nonlinear forward models that permit only black-box access (i.e., derivative-free). Theoretically, we establish a […]

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Autonomous machine learning agents have revolutionized scientific discovery, yet they remain constrained by a Generate-Execute-Feedback paradigm. Previous approaches suffer from a severe Execution Bottleneck, as hypothesis evaluation relies strictly on expensive physical execution. To bypass these physical constraints, we internalize execution priors to substitute costly runtime checks with instantaneous predictive reasoning, drawing inspiration from World Models. In this work, we formalize the task of Data-centric Solution Preference and construct a comprehensive corpus of 18,438 pairwise comparisons. We demonstrate […]

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Mistral-Medium-3.5-128B is a flagship 128B model for reasoning, coding, vision, function calling, and long-context enterprise AI.

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arXiv:2601.10740v1 Announce Type: new Abstract: Modern neural networks rely on generic activation functions (ReLU, GELU, SiLU) that ignore the mathematical structure inherent in scientific data. We propose Neuro-Symbolic Activation Discovery, a framework that uses Genetic Programming to extract interpretable mathematical formulas from data and inject them as custom activation functions. Our key contribution is the discovery of a Geometric Transfer phenomenon: activation functions learned from particle physics data successfully generalize to ecological classification, outperforming standard activations (ReLU, GELU, […]

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Playwright and Puppeteer are two of the most popular tools for browser automation, but they differ in performance and capabilities. In this article, we’ll compare them side by side to help you decide which one fits your needs. However, it’s important to remember that as websites become harder to access with automated scripts, code efficiency is only half the battle. That’s why we’ll also discuss how proxy solutions can help avoid interruptions and scale automation reliably, with links […]

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Release: datasette-files 0.1a2 The most interesting alpha of datasette-files yet, a new plugin which adds the ability to upload files directly into a Datasette instance. Here are the release notes in full: Columns are now configured using the new column_types system from Datasette 1.0a26. #8 New file_actions plugin hook, plus ability to import an uploaded CSV/TSV file to a table. #10 UI for uploading multiple files at once via the new documented JSON upload API. #11 Thumbnails are […]

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arXiv:2602.06175v2 Announce Type: replace-cross Abstract: Expand-and-sparsify representations are a class of theoretical models that capture sparse representation phenomena observed in the sensory systems of many animals. At a high level, these representations map an input $x in mathbb{R}^d$ to a much higher dimension $m gg d$ via random linear projections before zeroing out all but the $k ll m$ largest entries. The result is a $k$-sparse vector in ${0,1}^m$. We study the suitability of this representation for two […]

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