Chaojun Xiao

Recurrence's rapid learning of retrieval functions actively prevents attention from acquiring similar capabilities in hybrid architectures, necessitating staged training for optimal performance.

Aggressive KV cache compression can be achieved with near-zero accuracy loss by treating the cache as a trainable neural network optimized at test-time.

Chain-of-thought context can be halved without sacrificing accuracy by using the geometric trajectory of hidden states to selectively compress exploratory reasoning steps into continuous latent tokens.