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Multi-agent debate (MAD) aims to improve large language model reasoning by exposing agents to alternative solutions and enabling iterative revision. However, standard broadcast-style MAD makes peer answers visible without requiring agents to engage with a particular peer claim. As a result, reliable reasoning may be su...
This study develops NeuroSketch, a practical design recipe for neural decoding, through a basic architecture study followed by macro- and micro-level optimization, and finds that CNN-2D outperforms other architectures in neural decoding tasks and explores its effectiveness from temporal and spatial perspectives.
This work introduces Brain4FMs, the first unified benchmark, to the knowledge, for jointly evaluating BFMs on EEG and iEEG, and conducts exploratory analyses of model-specific properties of spatial, spectral, and discrete representations.
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