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We find that major reported improvements in decoding words from non-invasive brain recordings are largely reproducible without any brain data. In the influential work of d'Ascoli et al. (2025), time series of brain activity from subjects perceiving continuous speech are segmented into fixed-length windows starting at e...
HorizonMath, a benchmark of 113 predominantly unsolved problems spanning eight domains in mathematics and the mathematical sciences, paired with an open-source evaluation framework for automated verification, is introduced, identifying six novel solutions to research problems that either resolve previously open questio...
This work introduces Brain2Semantics2Text, a method that reconstructs text through an intermediate semantic embedding space and describes the core principles of the approach, its implementation, and the strategies used to mitigate the challenges of learning a reliable neural-to-semantic mapping.
The ambition of the 2025 PNPL competition (Landau et al., 2025) was to launch a multi-year curriculum for non-invasive speech decoding. Designed to progress from foundational tasks toward the linguistic complexity required for a practical brain-computer interface (BCI), it set the stage with speech detection and phonem...
Deriving open-vocabulary mutual information (OVMI), an information-theoretic quantity that measures the information conveyed by a decoder relative to a reference distribution over the words a user may wish to communicate, provides the speech BCI community with a principled way to compare heterogeneous systems, improve...
LibriBrain100 is introduced, a large-scale MEG dataset for speech decoding designed from the ground up for reproducible, standardised evaluation and the value of broad multi-subject data is demonstrated: supervised finetuning of a pre-trained model can substantially compensate for limited per-subject data.
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