technocracy

arXiv:2604.09682v1 Announce Type: new Abstract: Autonomous network management in Open Radio Access Networks requires intelligent decision making across conflicting objectives, yet existing LLM based multi agent systems employ homogeneous strategies and lack systematic predeployment validation. We introduce a persona driven multi agent framework where configurable behavioral personas structured specifications encoding optimization priorities, risk tolerance, and decision making style influence five specialized agents (planning, coordination, resource allocation, code generation, analysis). To enable rigorous validation, we develop a three dimensional […]

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arXiv:2603.03819v1 Announce Type: cross Abstract: Regression discontinuity designs (RDD) are widely used for causal inference. In many empirical applications, treatment effects vary substantially with covariates, and ignoring such heterogeneity can lead to misleading conclusions, which motivates flexible modeling of heterogeneous treatment effects in RDD. To this end, we propose a Bayesian nonparametric approach to estimating heterogeneous treatment effects based on Bayesian Additive Regression Trees (BART). The key feature of our method lies in adopting a general Bayesian framework […]

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arXiv:2509.11070v3 Announce Type: replace Abstract: We develop a stochastic approximation framework for learning nonlinear operators between infinite-dimensional spaces utilizing general Mercer operator-valued kernels. Our framework encompasses two key classes: (i) compact kernels, which admit discrete spectral decompositions, and (ii) diagonal kernels of the form $K(x,x’)=k(x,x’)T$, where $k$ is a scalar-valued kernel and $T$ is a positive operator on the output space. This broad setting induces expressive vector-valued reproducing kernel Hilbert spaces (RKHSs) that generalize the classical $K=kI$ paradigm, […]

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We study training algorithms with data following a Gaussian mixture model. For a specific family of such algorithms, we present a non-asymptotic result, connecting the evolution of the model to a surrogate dynamical system, which can be easier to analyze. The proof of our result is based on the celebrated Gordon comparison theorem. Using our theorem, we rigorously prove the validity of the dynamic mean-field (DMF) expressions in the asymptotic scenarios. Moreover, we suggest an iterative refinement scheme […]

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arXiv:2601.20916v1 Announce Type: new Abstract: Objective: Accurate noninvasive estimation of intracranial pressure (ICP) remains a major challenge in critical care. We developed a bespoke machine learning algorithm that integrates system identification and ranking-constrained optimization to estimate mean ICP from noninvasive signals. Methods: A machine learning framework was proposed to obtain accurate mean ICP values using arbitrary noninvasive signals. The subspace system identification algorithm is employed to identify cerebral hemodynamics models for ICP simulation using arterial blood pressure (ABP), […]

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arXiv:2601.17033v1 Announce Type: new Abstract: Big Deal cancellations are increasingly undertaken by academic librarians faced with rising subscription costs and shrinking collections budgets. While past research has focused on librarians’ decision-making processes and communication strategies, this study aims to understand the perspectives and experiences of faculty and graduate students with Big Deal cancellations through interviews at three medium-sized Canadian institutions. It considers cancellations as a collaborative process of information exchange, rather than a top-down process. This study’s findings […]

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It’s wild that the first commit to OpenClaw was on November 25th 2025, and less than three months later it’s hit 10,000 commits from 600 contributors, attracted 196,000 GitHub stars and sort-of been featured in an extremely vague Super Bowl commercial for AI.com. Quoting AI.com founder Kris Marszalek, purchaser of the most expensive domain in history for $70m: ai.com is the world’s first easy-to-use and secure implementation of OpenClaw, the open source agent framework that went viral two […]

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Reinforcement Learning (RL) has emerged as a dominant paradigm for end-to-end autonomous driving (AD). However, RL suffers from sample inefficiency and a lack of semantic interpretability in complex scenarios. Foundation Models, particularly Vision-Language Models (VLMs), can mitigate this by offering rich, context-aware knowledge, yet their high inference latency hinders deployment in high-frequency RL training loops. To bridge this gap, we present Found-RL, a platform tailored to efficiently enhance RL for AD using foundation models. A core innovation is […]

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We explore the Traveling Salesman Problem (TSP) using a Variational Quantum Algorithm (VQA), with a focus on representation efficiency and model structure learning rather than just parameter tuning. Key ideas: Compact permutation-based encoding Uses O(nlog⁡n)O(n log n)O(nlogn) qubits and guarantees that every quantum state corresponds to a valid tour (no constraint penalties or repair steps). Adaptive circuit optimization Instead of fixing the quantum circuit (ansatz) upfront, we optimize its structure using Simulated Annealing: add / remove rotation and […]

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