technocracy

arXiv:2602.10301v1 Announce Type: new Abstract: Among the different concepts for wave energy conversion, oscillating surge wave energy converters have been shown to have a high capture width ratio. The primary wave capture structure consists of a flap hinged at the seabed or to a floating platform. Different flap configurations, including single and dual-flap, have been investigated. The separation distance between the oscillating surge wave energy converters can have an impact on their response when deployed in arrays. We […]

Ver mais

Clarity doesn’t come from thinking; it comes from movement. The correct order is: Decide what you want to change Anchor it to a why that carries weight Let the RAS filter reality Let the how reveal itself through action What + Why (RAS) → How This is the neuroscience of momentum. – You know what you want to change. You’ve known for a while. You just haven’t moved on it. Your life works, and that’s part of the […]

Ver mais

USA AI Summit LLC officially announces the launch of the USA AI Summit 2026, a landmark gathering designed to unite thought leaders, developers, researchers, founders, and innovators at the forefront of artificial intelligence. Positioned as a leading USA artificial intelligence summit, the event will take place on November 7, 2026, at 515 Madison Avenue, New York, NY 10022 (Jay Suites – The Beatles Room). As a premier AI summit and dynamic artificial intelligence summit, the USA AI Summit […]

Ver mais

This article presents an argument for why quantum computers could unlock new methods for machine learning. We argue that spectral methods, in particular those that learn, regularise, or otherwise manipulate the Fourier spectrum of a machine learning model, are often natural for quantum computers. For example, if a generative machine learning model is represented by a quantum state, the Quantum Fourier Transform allows us to manipulate the Fourier spectrum of the state using the entire toolbox of quantum […]

Ver mais

Group Relative Policy Optimization (GRPO) significantly enhances the reasoning performance of Large Language Models (LLMs). However, this success heavily relies on expensive external verifiers or human rules. Such dependency not only leads to significant computational costs and training latency, but also yields sparse rewards that hinder optimization efficiency. To address these challenges, we propose Latent-GRPO, a framework that derives intrinsic rewards directly from latent space geometry. Crucially, our empirical analysis reveals a compelling geometric property: terminal token representations […]

Ver mais

Offline reinforcement learning (RL) provides a compelling paradigm for training autonomous systems without the risks of online exploration, particularly in safety-critical domains. However, jointly achieving strong safety and performance from fixed datasets remains challenging. Existing safe offline RL methods often rely on soft constraints that allow violations, introduce excessive conservatism, or struggle to balance safety, reward optimization, and adherence to the data distribution. To address this, we propose Epigraph-Guided Flow Matching (EpiFlow), a framework that formulates safe offline […]

Ver mais

Since the beginning of modern computer history, the Turing machine has been a dominant architecture for most computational devices, which consists of three essential components: an infinite tape for input, a read/write head, and finite control. In this structure, what the head can read (i.e., binary digits and symbols) is the same as what it has written/outputted. This is actually different from the ways in which humans think or do thought/tool experiments, where inputs can be abstract symbols […]

Ver mais