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By distilling a lightweight model Veilmind-4B to drive a dynamic extraction-sanitization-restoration pipeline, this approach reaches a low-leakage privacy point while preserving substantially higher response utility than existing privacy-oriented baselines, advancing the privacy-utility trade-off toward the Pareto frontier.
Low-frequency bias in EEG models can be corrected, leading to a significant boost in performance across a wide range of tasks.
Today's best AI agents can only solve 55% of real-world academic tasks that university students find challenging, revealing a significant gap between current AI capabilities and the demands of academic workflows.
