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cs.LG2026

When Graph Tokens Sink: A Mechanistic Analysis of Graph Language Models

Ding Zhang, Runtao Zhou, Wenqing Zheng +3

Graph Language Models (GLMs) have become a promising direction for adapting Large Language Models (LLMs) to graph learning tasks. By transforming graph topology and node informatio…

cs.LG2026

Zero-shot Multivariate Time Series Forecasting Using Tabular Prior Fitted Networks

Mayuka Jayawardhana, Nihal Sharma, Kazem Meidani +3

Tabular foundation models, particularly Prior-data Fitted Networks like TabPFN have emerged as the leading contender in a myriad of tasks ranging from data imputation to label pred…

cs.LG2024

A Simple Baseline for Predicting Events with Auto-Regressive Tabular Transformers

Alex Stein, Samuel Sharpe, Doron Bergman +5

Many real-world applications of tabular data involve using historic events to predict properties of new ones, for example whether a credit card transaction is fraudulent or what ra…

cs.LG2024

Searching for Efficient Linear Layers over a Continuous Space of Structured Matrices

Andres Potapczynski, Shikai Qiu, Marc Finzi +6

Dense linear layers are the dominant computational bottleneck in large neural networks, presenting a critical need for more efficient alternatives. Previous efforts focused on a sm…

cs.LG2024

AI versus AI in Financial Crimes and Detection: GenAI Crime Waves to Co-Evolutionary AI

Eren Kurshan, Dhagash Mehta, Bayan Bruss +1

Adoption of AI by criminal entities across traditional and emerging financial crime paradigms has been a disturbing recent trend. Particularly concerning is the proliferation of ge…