2 papers
cs.LG2026
Tensor Channel Equivariant Graph Neural Networks for Molecular Polarizability Prediction
Jean Philip Filling, Daniel Franzen, Michael Wand
We introduce a tensor-channel equivariant graph neural network for direct prediction of molecular polarizability tensors. Building on the efficient PaiNN architecture, we augment t…
cs.CL2025
Product of Experts with LLMs: Boosting Performance on ARC Is a Matter of Perspective
Daniel Franzen, Jan Disselhoff, David Hartmann
The Abstraction and Reasoning Corpus (ARC-AGI) poses a significant challenge for large language models (LLMs), exposing limitations in their abstract reasoning abilities. In this w…