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Harbin Institute of Technology
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Current memory retrieval methods for emotional support agents fall far short of human-level empathy, struggling to proactively retrieve memories that address users' latent emotional needs.
ShallowConvNet emerges as a surprisingly effective architecture for decoding user intent from EEG signals in real-world robotic control, outperforming more complex models like Transformers.
