technocracy

arXiv:2601.03309v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models, which integrate pretrained large Vision-Language Models (VLM) into their policy backbone, are gaining significant attention for their promising generalization capabilities. This paper revisits a fundamental yet seldom systematically studied question: how VLM choice and competence translate to downstream VLA policies performance? We introduce VLM4VLA, a minimal adaptation pipeline that converts general-purpose VLMs into VLA policies using only a small set of new learnable parameters for fair and efficient comparison. Despite […]

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arXiv:2601.14455v1 Announce Type: new Abstract: GitHub Actions is a widely used platform that allows developers to automate the build and deployment of their projects through configurable workflows. As the platform’s popularity continues to grow, it has become a target of choice for recent software supply chain attacks. These attacks exploit excessive permissions, ambiguous versions, or the absence of artifact integrity checks to compromise workflows. In response to these attacks, several security scanners have emerged to help developers harden […]

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Graph neural networks (GNNs) work remarkably well in semi-supervised node regression, yet a rigorous theory explaining when and why they succeed remains lacking. To address this gap, we study an aggregate-and-readout model that encompasses several common message passing architectures: node features are first propagated over the graph then mapped to responses via a nonlinear function. For least-squares estimation over GNNs with linear graph convolutions and a deep ReLU readout, we prove a sharp non-asymptotic risk bound that separates […]

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OpenAI has made tech waves in the recent years given the prominences of the ChatGPT family of models, and the remanent of LLMs as search engine reindexing algorithms. They were a private research entity that became a titan now competing with the likes of Google. However, their story is less than glamorous. They started out as a non-profit funded by Musk only to be insanely profit-driven. In fact, they are a cash-burn enterprise, and on top of that […]

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arXiv:2601.12288v1 Announce Type: new Abstract: Probabilistic time series forecasting is crucial for quantifying future uncertainty, with significant applications in fields such as energy and finance. However, existing methods often rely on computationally expensive sampling or restrictive parametric assumptions to characterize future distributions, which limits predictive performance and introduces distributional mismatch. To address these challenges, this paper presents TimeGMM, a novel probabilistic forecasting framework based on Gaussian Mixture Models (GMM) that captures complex future distributions in a single forward […]

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Biased gradient compression with error feedback (EF) reduces communication in federated learning (FL), but under non-IID data, the residual error can decay slowly, causing gradient mismatch and stalled progress in the early rounds. We propose step-ahead partial error feedback (SA-PEF), which integrates step-ahead (SA) correction with partial error feedback (PEF). SA-PEF recovers EF when the step-ahead coefficient $α=0$ and step-ahead EF (SAEF) when $α=1$. For non-convex objectives and $δ$-contractive compressors, we establish a second-moment bound and a residual […]

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The prediction of critical heat flux (CHF) using machine learning (ML) approaches has become a highly active research activity in recent years, the goal of which is to build models more accurate than current conventional approaches such as empirical correlations or lookup tables (LUTs). Previous work developed and deployed tube-based pure and hybrid ML models in the CTF subchannel code, however, full-scale reactor core simulations require the use of rod bundle geometries. Unlike isolated subchannels, rod bundles experience […]

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Many complex networks have partially observed or evolving connectivity, making link prediction a fundamental task. Topological link prediction infers missing links using only network topology, with applications in social, biological, and technological systems. The Cannistraci-Hebb (CH) theory provides a topological formulation of Hebbian learning, grounded on two pillars: (1) the minimization of external links within local communities, and (2) the path-based definition of local communities that capture homophilic (similarity-driven) interactions via paths of length 2 and synergetic (diversitydriven) […]

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