1 citations · 2 across the 6 of their papers we have counts for
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A Unifying Framework for Parallelizing Sequential Models with Linear Dynamical Systems
Xavier Gonzalez, E. Kelly Buchanan, Hyun Dong Lee +6
Harnessing parallelism in seemingly sequential models is a central challenge for modern machine learning. Several approaches have been proposed for evaluating sequential processes…
An Information Theoretic Perspective on Agentic System Design
Shizhe He, Avanika Narayan, Ishan S. Khare +3
Agentic language model (LM) systems power modern applications like "Deep Research" and "Claude Code," and leverage multi-LM architectures to overcome context limitations. Beneath t…
SING: SDE Inference via Natural Gradients
Amber Hu, Henry Smith, Scott Linderman
Latent stochastic differential equation (SDE) models are important tools for the unsupervised discovery of dynamical systems from data, with applications ranging from engineering t…
Informed Correctors for Discrete Diffusion Models
Yixiu Zhao, Jiaxin Shi, Feng Chen +3
Discrete diffusion has emerged as a powerful framework for generative modeling in discrete domains, yet efficiently sampling from these models remains challenging. Existing samplin…
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…
Towards Scalable and Stable Parallelization of Nonlinear RNNs
Xavier Gonzalez, Andrew Warrington, Jimmy T. H. Smith +1
Transformers and linear state space models can be evaluated in parallel on modern hardware, but evaluating nonlinear RNNs appears to be an inherently sequential problem. Recently,…