Interpretability of CS3 encoder latent features with respect to cognitive states and intentions

Characterize how the latent features learned by the CS3 encoder in LoongX relate to interpretable cognitive states or user intentions by establishing a clear mapping between neural-signal-derived representations and specific cognitive constructs or editing intents.

Background

The CS3 encoder is designed to extract multi-scale spatiotemporal features from diverse neural signals to condition a diffusion transformer for editing. Despite effective performance, the authors explicitly state a lack of clarity regarding how these latent features correspond to interpretable cognitive states or intentions.

Improving interpretability is essential for responsible deployment, transparency, and user trust in neural-driven systems.