technocracy

arXiv:2602.08287v1 Announce Type: cross Abstract: Understanding simplicity biases in deep learning offers a promising path toward developing reliable AI. A common metric for this, inspired by Boolean function analysis, is average sensitivity, which captures a model’s robustness to single-token perturbations. We argue that average sensitivity has two key limitations: it lacks a natural generalization to real-valued domains and fails to explain the “junta-like” input dependence we empirically observe in modern LLMs. To address these limitations, we propose noise […]

Ver mais

:::info Authors: Elizabeth A. Gibson Huichu Li Victoria Fruh Malaika Gabra Gowtham Asokan Anne Marie Z. Jukic Donna D. Baird Christine L. Curry Tyler Fischer-Colbrie Jukka-Pekka Onnela Michelle A. Williams Russ Hauser Brent A. Coull Shruthi Mahalingaiah ::: Abstract COVID-19 vaccination may be associated with change in menstrual cycle length following vaccination. We estimated covariate-adjusted differences in mean cycle length (MCL), measured in days, between pre-vaccination cycles, vaccination cycles, and post-vaccination cycles within vaccinated participants who met eligibility […]

Ver mais

The Day Your LLM Stops Talking and Starts Doing There’s a moment in every LLM project where you realize the “chat” part is the easy bit. The hard part is everything that happens between the user request and the final output: gathering missing facts, choosing which tools to call (and in what order), handling failures, remembering prior decisions, and not spiraling into confident nonsense when the world refuses to match the model’s assumptions. That’s the moment you’re no […]

Ver mais

Im super late to the party but I had completely forgotten about RL environments for agents since I first explored it due to mechanize.work. Now I see that meta has also been quite active in that area. I’m a mere layman in RL so could someone tell me how big of a deal this actually is and if it is an unsolved problem or just an implementation/business problem? submitted by /u/lokeye-ai [link] [comments]

Ver mais

Flooding is the most pervasive natural disaster worldwide. Timely and accurate flood inundation mapping are essential for informing disaster risk management. Optical satellite missions provide high-resolution, multispectral observations critical for flood detection and inundation mapping. However, their operational utility is severely constrained by cloud cover during extreme precipitation events. Conventional cloud-removal techniques based on temporal compositing or interpolation often fail to capture inundation dynamics. In this study, we introduce a cloud-removal framework for flood imagery based on Denoising […]

Ver mais

A fundamental challenge in multi-task reinforcement learning (MTRL) is achieving sample efficiency in visual domains where tasks exhibit substantial heterogeneity in both observations and dynamics. Model-based reinforcement learning offers a promising path to improved sample efficiency through world models, but standard monolithic architectures struggle to capture diverse task dynamics, resulting in poor reconstruction and prediction accuracy. We introduce Mixture-of-World Models (MoW), a scalable architecture that combines modular variational autoencoders for task-adaptive visual compression, a hybrid Transformer-based dynamics model […]

Ver mais

arXiv:2602.18541v1 Announce Type: new Abstract: Large Language Models (LLMs) increasingly serve as consumers of API specifications, whether for code generation, autonomous agent interaction, or API-assisted reasoning. The de facto standard for API description, OpenAPI, was designed for documentation tools and code generators, resulting in substantial token overhead when used as LLM context. We present LAPIS (Lightweight API Specification for Intelligent Systems), a domain-specific format optimized for LLM consumption that preserves the semantic information necessary for API reasoning while […]

Ver mais

arXiv:2602.10611v1 Announce Type: cross Abstract: Physics-informed neural networks (PINNs) have gained significant attention as a surrogate modeling strategy for partial differential equations (PDEs), particularly in regimes where labeled data are scarce and physical constraints can be leveraged to regularize the learning process. In practice, however, PINNs are frequently trained using experimental or numerical data that are not fully consistent with the governing equations due to measurement noise, discretization errors, or modeling assumptions. The implications of such data-to-PDE inconsistencies […]

Ver mais