EEG foundation models show no single winner across failure modes, attend to correct brain regions but decode corrupted signals, and retain task information in early layers while late layers adapt during fine-tuning.
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics , year =
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
A textbook exposition that organizes the vision-language model landscape into a modular encode–reason–decode framework and surveys architectures, losses, data, evaluation, and applications, without introducing new results.
citing papers explorer
-
Beyond Accuracy: Robustness, Interpretability and Expressiveness of EEG Foundation Models
EEG foundation models show no single winner across failure modes, attend to correct brain regions but decode corrupted signals, and retain task information in early layers while late layers adapt during fine-tuning.
-
From Pixels to Prompts: Vision-Language Models
A textbook exposition that organizes the vision-language model landscape into a modular encode–reason–decode framework and surveys architectures, losses, data, evaluation, and applications, without introducing new results.