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cs.LG2026
Crystalite: A Lightweight Transformer for Efficient Crystal Modeling
Tin Hadži Veljković, Joshua Rosenthal, Ivor Lončarić +1
Generative models for crystalline materials often rely on equivariant graph neural networks, which capture geometric structure well but are costly to train and slow to sample. We p…
cs.LG2026
[Re] Benchmarking LLM Capabilities in Negotiation through Scoreable Games
Jorge Carrasco Pollo, Ioannis Kapetangeorgis, Joshua Rosenthal +1
Large Language Models (LLMs) demonstrate significant potential in multi-agent negotiation tasks, yet evaluation in this domain remains challenging due to a lack of robust and gener…
cs.LG2025
Symmetry-Aware Graph Metanetwork Autoencoders: Model Merging through Parameter Canonicalization
Odysseas Boufalis, Jorge Carrasco-Pollo, Joshua Rosenthal +2
Neural network parameterizations exhibit inherent symmetries that yield multiple equivalent minima within the loss landscape. Scale Graph Metanetworks (ScaleGMNs) explicitly levera…