technocracy

This research note identifies a previously overlooked distinction between multi-objective reinforcement learning (MORL), and more conventional single-objective reinforcement learning (RL). It has previously been noted that the optimal policy for an MORL agent with a non-linear utility function is required to be conditioned on both the current environmental state and on some measure of the previously accrued reward. This is generally implemented by concatenating the observed state of the environment with the discounted sum of previous rewards to […]

Ver mais

arXiv:2601.07876v1 Announce Type: new Abstract: This work introduces NOVAK, a modular gradient-based optimization algorithm that integrates adaptive moment estimation, rectified learning-rate scheduling, decoupled weight regularization, multiple variants of Nesterov momentum, and lookahead synchronization into a unified, performance-oriented framework. NOVAK adopts a dual-mode architecture consisting of a streamlined fast path designed for production. The optimizer employs custom CUDA kernels that deliver substantial speedups (3-5 for critical operations) while preserving numerical stability under standard stochastic-optimization assumptions. We provide fully developed […]

Ver mais

arXiv:2604.06265v1 Announce Type: new Abstract: Quantum-inspired tensor networks algorithms have shown to be effective and efficient models for machine learning tasks, including anomaly detection. Here, we propose a highly parallelizable quantum-inspired approach which we call SMT-AD from Superposition of Multiresolution Tensors for Anomaly Detection. It is based upon the superposition of bond-dimension-1 matrix product operators to transform the input data with Fourier-assisted feature embedding, where the number of learnable parameters grows linearly with feature size, embedding resolutions, and […]

Ver mais

arXiv:2602.18467v1 Announce Type: new Abstract: This study explores the relationship between body composition metrics, self-compassion, and social support among college students. Using seasonal body composition data from the InBody770 system and psychometric measures from the Lived Experiences Measured Using Rings Study (LEMURS) (n=156; freshmen=66, sophomores=90), Canonical Correlation Analysis (CCA) reveals body composition metrics exhibit moderate correlation with self-compassion and social support. Certain physiological and psychological features showed strong and consistent relationships with well-being across the academic year. Trunk […]

Ver mais

Release: datasette-files 0.1a2 The most interesting alpha of datasette-files yet, a new plugin which adds the ability to upload files directly into a Datasette instance. Here are the release notes in full: Columns are now configured using the new column_types system from Datasette 1.0a26. #8 New file_actions plugin hook, plus ability to import an uploaded CSV/TSV file to a table. #10 UI for uploading multiple files at once via the new documented JSON upload API. #11 Thumbnails are […]

Ver mais

arXiv:2601.15506v1 Announce Type: new Abstract: Drawing inspiration from recent findings including surprisingly decent performance of transformers without positional encoding (NoPE) in the domain of language models and how registers (additional throwaway tokens not tied to input) may improve the performance of large vision transformers (ViTs), we invent and test a variant of ViT called fractal ViT that breaks permutation invariance among the tokens by applying an attention mask between the regular tokens and “summary tokens” similar to registers, […]

Ver mais

Self-correction is essential for solving complex reasoning problems in vision-language models (VLMs). However, existing reinforcement learning (RL) methods struggle to learn it, as effective self-correction behaviors emerge only rarely, making learning signals extremely sparse. To address this challenge, we propose correction-specific rollouts (Octopus), an RL rollout augmentation framework that synthesizes dense self-correction examples by recombining existing rollouts. This augmentation simultaneously improves sample efficiency due to rollout reuse and stabilizes RL optimization through balanced supervision. Furthermore, we introduce a […]

Ver mais

arXiv:2603.25834v1 Announce Type: new Abstract: Recent advances in parallel computing and GPU acceleration have created new opportunities for computation-intensive learning problems such as Active SLAM — where actions are selected to reduce uncertainty and improve joint mapping and localization. However, existing DRL-based approaches remain constrained by the lack of scalable parallel training. In this work, we address this challenge by proposing a scalable end-to-end DRL framework for Active SLAM that enables massively parallel training. Compared with the state […]

Ver mais

arXiv:2502.04907v4 Announce Type: replace Abstract: We consider statistical learning problems in which data are observed as a set of probability measures. Optimal transport (OT) is a popular tool to compare and manipulate such objects, but its computational cost becomes prohibitive when the measures have large support. We study a quantization-based approach in which all input measures are approximated by $K$-point discrete measures sharing a common support. We establish consistency of the resulting quantized measures. We further derive convergence […]

Ver mais