Yatao Bian

Hallucinations in chemical reasoning models coexist with correct answers, revealing a complex relationship that challenges our understanding of model reliability.

SinAE achieves near-lossless reconstruction across diverse atomic systems, drastically improving generative performance while simplifying the architecture.

Surpassing traditional Monte Carlo methods, this approach offers a stable and efficient alternative for learning neural set functions, dramatically cutting down computational costs.