technocracy

arXiv:2602.18492v1 Announce Type: new Abstract: Large Language Models (LLMs) are now good enough at coding that developers can describe intent in plain language and let the tool produce the first code draft, a workflow increasingly built into tools like GitHub Copilot, Cursor, and Replit. What is missing is a reliable way to tell which model written queries are safe to accept without sending everything to a human. We study the application of an LLM jury to run this […]

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arXiv:2411.01563v3 Announce Type: replace-cross Abstract: In recent years, denoising diffusion models have become a crucial area of research due to their abundance in the rapidly expanding field of generative AI. While recent statistical advances have delivered explanations for the generation ability of idealised denoising diffusion models for high-dimensional target data, implementations introduce thresholding procedures for the generating process to overcome issues arising from the unbounded state space of such models. This mismatch between theoretical design and implementation of […]

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In the world of security and DevOps, AI agents are being pushed from demos into production quickly. They triage security alerts, coordinate incident response, provision infrastructure, and decide which remediation playbooks to run. When it all works, everyone is happy. It’s a force multiplier. But when an AI agent fails … who do you blame? It can be hard to tell who made the call, why it happened, or what evidence exists to explain it. This is even […]

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arXiv:2603.03613v1 Announce Type: new Abstract: The No Free Lunch (NFL) theorem guarantees equal average performance only under uniform sampling of a function space closed under permutation (c.u.p.). We ask when this averaging ceases to reflect what benchmarking actually reports. We study an iterative-search setting with sampling without replacement, where algorithms differ only in evaluation order. Binary objectives allow exhaustive evaluation in the fully enumerable case, and efficiency is defined by the first time the global minimum is reached. […]

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Class-Incremental Learning (CIL) requires models to learn new classes without forgetting old ones. A common method is to freeze a pre-trained model and train a new, lightweight adapter for each task. While this prevents forgetting, it treats the learned knowledge as a simple, unstructured collection and fails to use the relationships between tasks. To this end, we propose the Semantic-guided Adaptive Expert Forest (SAEF), a new method that organizes adapters into a structured hierarchy for better knowledge sharing. […]

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arXiv:2511.16976v2 Announce Type: replace-cross Abstract: Deep equilibrium models (DEQs) have recently emerged as a powerful paradigm for training infinitely deep weight-tied neural networks that achieve state of the art performance across many modern machine learning tasks. Despite their practical success, theoretically understanding the gradient descent dynamics for training DEQs remains an area of active research. In this work, we rigorously study the gradient descent dynamics for DEQs in the simple setting of linear models and single-index models, filling […]

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arXiv:2602.04886v1 Announce Type: cross Abstract: Normative modeling estimates reference distributions of biological measures conditional on covariates, enabling centiles and clinically interpretable deviation scores to be derived. Most neuroimaging pipelines fit one model per imaging-derived phenotype (IDP), which scales well but discards multivariate dependence that may encode coordinated patterns. We propose denoising diffusion probabilistic models (DDPMs) as a unified conditional density estimator for tabular IDPs, from which univariate centiles and deviation scores are derived by sampling. We utilise two […]

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How Governance Turns AI Agent Experiments into Predictable Business Outcomes 1. The Strategic Inflection Point: From AI Tools to an Autonomous Workforce As we approach 2026, enterprises worldwide face a pivotal moment – the dawn of the “agent era.” Companies are swiftly transitioning from basic generative AI applications to sophisticated autonomous systems capable of handling complex business tasks. This evolution has unveiled a critical challenge: the “paradox of increasing investment versus uncertain returns.” Despite widespread adoption, with 79% […]

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