Robustness of LoongX under distribution shifts and noise

Ascertain the robustness of LoongX to varying data distributions and noisy conditions, including motion artifacts, sensor dropout, and environmental interference, through systematic stress testing of the multimodal neural-signal conditioning pipeline.

Background

LoongX fuses EEG, fNIRS, PPG, and motion signals via the proposed CS3 encoder and DGF module to guide image editing. While multimodal design suggests resilience, the authors explicitly state that robustness has not been systematically explored.

Systematic stress testing across realistic noise sources and distribution shifts is necessary to validate reliability for real-world deployment.