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Mamba-based foundation models for MEG outperform transformers in generative fidelity and training efficiency, unlocking longer context modeling of brain activity.
This work introduces a unified multi-agent framework, MAGS, that generates executable programs with formal safety guarantees, using Dafny as a verification-aware intermediate representation where safety properties can be mechanically checked.
Practical non-invasive BCIs cannot require days of user calibration, and this benchmark forces models to tackle word-level MEG decoding under strict 10-to-40-minute subject adaptation budgets.
Achieving state-of-the-art performance in speech decoding with a dataset that features 80 hours of deep, within-subject MEG data sets a new standard for neural data quality and quantity.
