Ziyuan Huang

LVLMs can achieve SOTA visual reasoning by learning to "see" in a way that optimizes for reasoning, even if it means deviating from strict geometric accuracy.

Doubling the number of tokens in a ViT-based autoencoder, combined with staged compression and self-supervised pretraining, dramatically improves generative performance under deep compression, without increasing the latent budget.