technocracy

arXiv:2503.12634v2 Announce Type: replace-cross Abstract: We develop Clustered Random Forests, a random forests algorithm for clustered data, arising from independent groups that exhibit within-cluster dependence. The leaf-wise predictions for each decision tree making up clustered random forests takes the form of a weighted least squares estimator, which leverage correlations between observations for improved prediction accuracy and tighter confidence intervals when performing inference. We show that approximately linear time algorithms exist for fitting classes of clustered random forests, matching […]

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Most foundation models in biology have a fundamental blind spot: they see cells as frozen snapshots. Give a model a single-cell transcriptome — a readout of which genes are active in a cell at a given moment — and it can tell you a lot about what that cell is doing right now. What it can’t tell you is where that cell is headed. That limitation matters enormously when studying aging. Age-related diseases like heart disease, Alzheimer’s dementia, […]

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The AI arms race is a trillion-dollar game of follow-the-leader, and everyone’s running in the wrong direction. While OpenAI, Google, and Anthropic pour billions into making models 0.3% more accurate on benchmarks nobody cares about, a quiet revolution is brewing in Discord servers, GitHub repos, and weekend hack projects. These indie developers aren’t trying to build better AI. They’re building AI that actually matters. The Benchmark Trap Here’s what Big AI won’t tell you: the race to dominate […]

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The calculation of shortest-path distances in road networks is a core operation in navigation systems, location-based services, and spatial analytics. Although classical algorithms, e.g., Dijkstra’s algorithm, provide exact answers, their latency is prohibitive for modern real-time, large-scale deployments. Over the past two decades, numerous distance indexes have been proposed to speed up query processing for shortest distance queries. More recently, with the advancement in machine learning (ML), researchers have designed and proposed ML-based distance indexes to answer approximate […]

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arXiv:2602.07213v1 Announce Type: new Abstract: Large Language Models (LLMs) often falter in complex reasoning tasks due to their static, parametric knowledge, leading to hallucinations and poor performance in specialized domains like mathematics. This work explores a fundamental principle for enhancing generative models: treating retrieval as a form of dynamic in-context learning. We test an adaptive retrieval-augmented architecture where an LLM agent actively decides when to query an external knowledge base during its reasoning process. We compare this adaptive […]

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arXiv:2602.08998v4 Announce Type: replace-cross Abstract: We study homology of ample groupoids via the compactly supported Moore complex of the nerve. Let $A$ be a topological abelian group. For $nge 0$ set $C_n(mathcal G;A) := C_c(mathcal G_n,A)$ and define $partial_n^A=sum_{i=0}^n(-1)^i(d_i)_*$. This defines $H_n(mathcal G;A)$. The theory is functorial for continuous ‘etale homomorphisms. It is compatible with standard reductions, including restriction to saturated clopen subsets. In the ample setting it is invariant under Kakutani equivalence. We reprove Matui type long […]

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arXiv:2603.20220v1 Announce Type: new Abstract: The integration of artificial intelligence (AI) in higher education underscores the growing importance of faculty AI literacy and competency across teaching, research, and service. Existing AI literacy instruments, however, primarily target the general public, students, or K-12 teachers, and therefore lack the role-embedded indicators and psychometric validation needed for scalable assessment among university faculty. Grounded in the Critical Tech-resilient Literacies (CTRL) framework, this study develops and validates the Faculty Artificial Intelligence Literacy and […]

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arXiv:2601.21470v1 Announce Type: cross Abstract: We study semi-supervised stochastic optimization when labeled data is scarce but predictions from pre-trained models are available. PPI and SVRG both reduce variance through control variates — PPI uses predictions, SVRG uses reference gradients. We show they are mathematically equivalent and develop PPI-SVRG, which combines both. Our convergence bound decomposes into the standard SVRG rate plus an error floor from prediction uncertainty. The rate depends only on loss geometry; predictions affect only the […]

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There was no outage. No alerts. n No failed pipelines. Everything “worked.” But our BI environment was slowing down, getting expensive, impossible to budget for. Not because the warehouse was failing. Because the design was wrong. AI will expose that at scale. The Architecture That Punished Curiosity. For years, the default in modern BI orgs was “DirectQuery”. Users → Reports → Semantic Model → Warehouse → Execution → Results. Every click triggered compute. It felt good. It felt […]

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