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20232026
most citedJust read twice: closing the recall gap for recurrent language models

1 citations · 1 across the 9 of their papers we have counts for

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cs.LG2026

FlashRT: Agent Harness for Guiding Agents to Deploy Real-Time Multimodal Applications

Krish Agarwal, Zhuoming Chen, Yanyuan Qin +3

Real-time multimodal applications, including voice agents and interactive video generation, compose heterogeneous models into pipelines whose efficient deployment requires applicat…

cs.LG2026

MLPs are Hebbians: Constructing Efficient Fact-Storing MLPs for Transformers

Roberto Garcia, Jerry Liu, Ronny Junkins +3

Large language models (LLMs) store factual knowledge in their parameters. While recent work has shown that this knowledge resides in MLP layers, existing constructive and mechanist…

cs.LG2025

Constructing Efficient Fact-Storing MLPs for Transformers

Owen Dugan, Roberto Garcia, Ronny Junkins +5

The success of large language models (LLMs) can be attributed in part to their ability to efficiently store factual knowledge as key-value mappings within their MLP parameters. Rec…

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.LG2025

Towards Learning High-Precision Least Squares Algorithms with Sequence Models

Jerry Liu, Jessica Grogan, Owen Dugan +4

This paper investigates whether sequence models can learn to perform numerical algorithms, e.g. gradient descent, on the fundamental problem of least squares. Our goal is to inheri…