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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.
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.
Standard word error rates artificially inflate speech BCI performance by ignoring unmodeled language, masking a critical capability trade-off that an open-vocabulary information-theoretic metric resolves while improving decoding accuracy by up to 16.3%.
GPT 5.4 Pro may have made novel contributions to mathematics, outperforming published results on two unsolved problems, as measured by the new HorizonMath benchmark.
