technocracy

arXiv:2505.21813v2 Announce Type: replace-cross Abstract: Data Augmentation (DA) has become an essential tool to improve robustness and generalization of modern machine learning. However, when deciding on DA strategies it is critical to choose parameters carefully, and this can be a daunting task which is traditionally left to trial-and-error or expensive optimization based on validation performance. In this paper, we counter these limitations by proposing a novel framework for optimizing DA. In particular, we take a probabilistic view of […]

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Hey im writing my bachlor thesis in RL. I modified ppo and want to give context to my results. I testet my algo vs ppo, but i cant find any sources to validate my base score. Where are you looking for references? Important note, im using the continius actionspace of LunarLander v3. submitted by /u/rclarsfull [link] [comments]

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Table of Links ABSTRACT 1 INTRODUCTION 2 INTERACTIVE WORLD SIMULATION 3 GAMENGEN 3.1 DATA COLLECTION VIA AGENT PLAY 3.2 TRAINING THE GENERATIVE DIFFUSION MODEL 4 EXPERIMENTAL SETUP 4.1 AGENT TRAINING 4.2 GENERATIVE MODEL TRAINING 5 RESULTS 5.1 SIMULATION QUALITY 5.2 ABLATIONS 6 RELATED WORK 7 DISCUSSION, ACKNOWLEDGEMENTS AND REFERENCES 2 INTERACTIVE WORLD SIMULATION An Interactive Environment E consists of a space of latent states S, a space of partial projections of the latent space O, a partial projection […]

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arXiv:2601.15324v1 Announce Type: new Abstract: Adding memory to pretrained language models typically requires architectural changes or weight modification. We present Prometheus Mind, which retrofits memory to a frozen Qwen3-4B using 11 modular adapters (530MB, 7% overhead) — fully reversible by removing the adapters. Building this system required solving four problems: (1) Extraction — we develop Contrastive Direction Discovery (CDD), which finds semantic directions via minimal pairs without labeled data. (2) Training — end-to-end optimization collapses; stage-wise training of […]

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Laplace learning is a semi-supervised method, a solution for finding missing labels from a partially labeled dataset utilizing the geometry given by the unlabeled data points. The method minimizes a Dirichlet energy defined on a (discrete) graph constructed from the full dataset. In finite dimensions the asymptotics in the large (unlabeled) data limit are well understood with convergence from the graph setting to a continuum Sobolev semi-norm weighted by the Lebesgue density of the data-generating measure. The lack […]

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BBVA is expanding its work with OpenAI through a multi-year AI transformation program, rolling out ChatGPT Enterprise to all 120,000 employees. Together, the companies will develop AI solutions that enhance customer interactions, streamline operations, and help build an AI-native banking experience.

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A few weeks into running my scene compiler at scale, I hit an embarrassing pattern: I’d ask for five candidates, score them, and the “top five” would be… the same idea, phrased five ways. Not bad ideas—often the highest composite scores—but clustered so tightly that the selection step was basically doing beam search without admitting it. The result felt like creative mode collapse: the system wasn’t choosing the best set of candidates, it was choosing the best single […]

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Multi-task learning shows strikingly inconsistent results — sometimes joint training helps substantially, sometimes it actively harms performance — yet the field lacks a principled framework for predicting these outcomes. We identify a fundamental but unstated assumption underlying gradient-based task analysis: tasks must share training instances for gradient conflicts to reveal genuine relationships. When tasks are measured on the same inputs, gradient alignment reflects shared mechanistic structure; when measured on disjoint inputs, any apparent signal conflates task relationships with […]

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