technocracy

Everyone believes that the digits of constants such as π or √2 cannot be distinguished from a sequence of random bits. The first few trillion successfully pass all tests of randomness. However, proving that they indeed behave perfectly randomly is arguably one of the oldest and most difficult unsolved math conjectures. So far, nobody succeeded in proving even the most basic facts for any of these constants, for instance: In the binary digits of π, is the proportion […]

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arXiv:2603.20239v1 Announce Type: new Abstract: 3D Scene Graphs (3DSGs) provide hierarchical, multi-resolution abstractions that encode the geometric and semantic structure of an environment, yet their treatment of dynamics remains limited to tracking individual agents. Maps of Dynamics (MoDs) complement this by modeling aggregate motion patterns, but rely on uniform grid discretizations that lack semantic grounding and scale poorly. We present Rheos, a framework that explicitly embeds continuous directional motion models into an additional dynamics layer of a hierarchical […]

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Existing neural solvers for vehicle routing problems (VRPs) are typically trained either in a one-off manner on a fixed set of pre-defined tasks or in a lifelong manner on several tasks arriving sequentially, assuming sufficient training on each task. Both settings overlook a common real-world property: problem patterns may drift continually over time, yielding massive tasks sequentially arising while offering only limited training resources per task. In this paper, we study a novel lifelong learning paradigm for neural […]

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Primary bottleneck for Enterprise AI is not the availability of tools or the identification of a tech stack, it is getting the data landscape in order. Success in 2026 is predicated on having total clarity of the underlying data infrastructure and establishing a foundation that is petabyte-scale, secure, and high-performing. Without a reliable data layer, AI initiatives remain experimental rather than transformational. Foundation (Scalable and Maintainable Data Acquisition) A useful litmus test for the engineering foundation is time […]

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Learning from electronic health records (EHRs) time series is challenging due to irregular sam- pling, heterogeneous missingness, and the resulting sparsity of observations. Prior self-supervised meth- ods either impute before learning, represent missingness through a dedicated input signal, or optimize solely for imputation, reducing their capacity to efficiently learn representations that support clinical downstream tasks. We propose the Augmented-Intrinsic Dual-Masked Autoencoder (AID-MAE), which learns directly from incomplete time series by applying an intrinsic missing mask to represent naturally […]

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arXiv:2603.23690v1 Announce Type: new Abstract: Modern manufacturing under High-Mix-Low-Volume requirements increasingly relies on flexible and adaptive matrix production systems, which depend on interconnected heterogeneous devices and rapid task reconfiguration. To address these needs, we present ROSCell, a ROS2-based framework that enables the flexible formation and management of a computing continuum across various devices. ROSCell allows users to package existing robotic software as deployable skills and, with simple requests, assemble isolated cells, automatically deploy skill instances, and coordinate their […]

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arXiv:2603.26775v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) adapt to visual tasks via in-context learning (ICL), which relies heavily on demonstration quality. The dominant demonstration selection strategy is unsupervised k-Nearest Neighbor (kNN) search. While simple, this similarity-first approach is sub-optimal for complex factual regression tasks; it selects redundant examples that fail to capture the task’s full output range. We reframe selection as a sequential decision-making problem and introduce Learning to Select Demonstrations (LSD), training a Reinforcement […]

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arXiv:2602.20222v1 Announce Type: new Abstract: The Transparency and Consent Framework (TCF), developed by the Interactive Advertising Bureau (IAB) Europe, provides a de facto standard for requesting, recording, and managing user consent from European end-users. This framework has previously been found to infringe European data protection law and has subsequently been regularly updated. Previous research on the TCF focused exclusively on web contexts, with no attention given to its implementation in mobile applications. No work has systematically studied the […]

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arXiv:2407.19892v2 Announce Type: replace Abstract: Motivation: Networks underlie the generation and interpretation of many biological datasets: gene networks shed light on the regulatory structure of the genome, and cell networks can capture structure of the tumor micro-environment. However, most methods that learn such networks make the faulty ‘independence assumption’; to learn the gene network, they assume that no cell network exists. ‘Multi-axis’ methods, which do not make this assumption, fail to scale beyond a few thousand cells or […]

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