3 papers
cs.LG2025
BWLer: Barycentric Weight Layer Elucidates a Precision-Conditioning Tradeoff for PINNs
Jerry Liu, Yasa Baig, Denise Hui Jean Lee +3
Physics-informed neural networks (PINNs) offer a flexible way to solve partial differential equations (PDEs) with machine learning, yet they still fall well short of the machine-pr…
cs.CL2025
Cartridges: Lightweight and general-purpose long context representations via self-study
Sabri Eyuboglu, Ryan Ehrlich, Simran Arora +8
Large language models are often used to answer queries grounded in large text corpora (e.g. codebases, legal documents, or chat histories) by placing the entire corpus in the conte…
cs.LG2025
Minions: Cost-efficient Collaboration Between On-device and Cloud Language Models
Avanika Narayan, Dan Biderman, Sabri Eyuboglu +4
We investigate an emerging setup in which a small, on-device language model (LM) with access to local data communicates with a frontier, cloud-hosted LM to solve real-world tasks i…