technocracy

arXiv:2604.02375v1 Announce Type: new Abstract: Tool-calling autonomous agents based on large language models using ReAct exhibit three limitations: serial latency, quadratic context growth, and vulnerability to prompt injection and hallucination. Recent work moves towards separating planning from execution but in each case the model remains coupled to the execution mechanics. We introduce a system-level abstraction for LLM agents which decouples the execution of agent workflows from the LLM reasoning layer. We define two first-class abstractions: (1) Intent-Gated Execution […]

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arXiv:2603.22473v1 Announce Type: new Abstract: Hybrid language models combining attention with state space models (SSMs) or linear attention offer improved efficiency, but whether both components are genuinely utilized remains unclear. We present a functional component ablation framework applied to two sub-1B hybrid models — Qwen3.5-0.8B (sequential: Gated DeltaNet + softmax attention) and Falcon-H1-0.5B (parallel: Mamba-2 + attention) — with a pure Transformer control (Qwen2.5-0.5B). Through group ablations, layer-wise sweeps, positional ablations, matched random controls, and perplexity analysis across […]

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arXiv:2602.21462v1 Announce Type: cross Abstract: We describe extensive numerical experiments assessing and quantifying how classifier performance depends on the quality of the training data, a frequently neglected component of the analysis of classifiers. More specifically, in the scientific context of metagenomic assembly of short DNA reads into “contigs,” we examine the effects of degrading the quality of the training data by multiple mechanisms, and for four classifiers — Bayes classifiers, neural nets, partition models and random forests. We […]

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I’m trying to deeply understand the mechanism behind LLM reasoning (specifically in models like o1 or DeepSeek). Mechanism: Is the model actually applying logic gates/rules, or is it just a probabilistic simulation of a logic path? If it “backtracks” during CoT, is that a learned pattern or a genuine evaluation of truth? And how close is this to AGI/Human level reasoning? The Data Wall: How much of current training is purely public (Common Crawl) vs private? Is the […]

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arXiv:2603.26803v1 Announce Type: cross Abstract: Physics-informed neural networks (PINNs) offer a unified framework for solving both forward and inverse problems of differential equations, yet their performance and physical consistency strongly depend on how governing laws are incorporated. In this work, we present a systematic comparison of different thermodynamic structure-informed neural networks by incorporating various thermodynamics formulations, including Newtonian, Lagrangian, and Hamiltonian mechanics for conservative systems, as well as the Onsager variational principle and extended irreversible thermodynamics for dissipative […]

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This is the difference between Data and a large language model, at least the ones operating right now. Data created art because he wanted to grow. He wanted to become something. He wanted to understand. Art is the means by which we become what we want to be. […] The book, the painting, the film script is not the only art. It’s important, but in a way it’s a receipt. It’s a diploma. The book you write, the […]

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Harmful algal blooms (HABs) can threaten coastal infrastructure, fisheries, and desalination dependent water supplies. This project (REDNET-ML) develops a reproducible machine learning pipeline for HAB risk detection along the Omani coastline using multi sensor satellite data and non leaky evaluation. The system fuses (i) Sentinel-2 optical chips (high spatial resolution) processed into spectral indices and texture signals, (ii) MODIS Level-3 ocean color and thermal indicators, and (iii) learned image evidence from object detectors trained to highlight bloom like […]

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arXiv:2512.08179v2 Announce Type: replace-cross Abstract: We study estimation of the conditional law $P(Y|X=x)$ and continuous functionals $Psi(P(Y|X=x))$ when $Y$ takes values in a locally compact Polish space, $X in mathbb{R}^p$, and the observations arise from a complex survey design. We propose a survey-calibrated distributional random forest (SDRF) that incorporates complex-design features via a pseudo-population bootstrap, PSU-level honesty, and a Maximum Mean Discrepancy (MMD) split criterion computed from kernel mean embeddings of H'{a}jek-type (design-weighted) node distributions. We provide a […]

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