Modern multimodal models bring generation and understanding into a single unified system, which enables them to provide and learn from their own feedback. Motivated by this unified capacity, we introduce UniEvo-VL, a self-evolving framework for multimodal models to learn from this constructive self-correction feedback...
Fang Wu, Dan-Lei Xing, Yan-Jie Huang et al.· 0 citations
This work introduces AutoScreen, an AI co-scientist system supporting target discovery through both Pre-screen Design, which constructs perturbation libraries de novo from free-text research descriptions, and Post-screen Analysis, which re-ranks experimental screen hits by integrating statistical scores with biological...
Yuan-Hao Qu, Xu-Feng Liu, Xiao-Tong Wang et al.· bioRxiv· 0 citations
As the supply of human-written text is exhausted, it has become standard practice to repeat language model training data. Prior work has studied data repetition for densely activated Transformers, but the effects of data repetition remains largely unexplored for recently dominant sparse architectures such as Mixture-of...
Atindra Jha, Margaret Li, J. Leskovec et al.· 1 citation
Biomni envisions artificial intelligence augmenting human scientists and accelerating discovery by interpreting multi-modal datasets, optimizing protein stability, orchestrating wet-lab instruments, and generating experimentally testable protocols.
Kexin Huang, Serena Zhang, Hanchen Wang et al.· Science· 19 citations· ⚡2
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