Yawei Li

S-EDL: Eliciting Self-Evidence from Sequence Likelihoods for Semantic Calibration of LLMs

Yawei Li, Jiazheng Li, David Rügamer, et al.

LuMamba: Latent Unified Mamba for Electrode Topology-Invariant and Efficient EEG Modeling

Danaé Broustail, Anna Tegon, Thorir Mar Ingolfsson, et al.

LuMamba is a new AI model designed to analyze brain activity data (EEG) more efficiently and flexibly than previous approaches. It solves two key problems: it works with different numbers and arrangements of brain sensors, and it processes information much faster (377× fewer computations) than existing models by using a modern architecture called Mamba instead of Transformers. Trained on over 21,000 hours of unlabeled EEG data, the model achieves state-of-the-art results on medical tasks like detecting Alzheimer's disease while being compact enough to run practically.