Recent advances in foundation models and large-scale cellular perturbation datasets have driven growing interest in using LLMs to predict and reason about cellular responses. Biological reasoning in this setting involves relating molecular mechanisms, cellular responses, and functional outcomes across scales. Evaluatin...
Xue Xia, Yue Wang, Shui-Zhou Chen et al.· 0 citations
On-policy self-distillation (OPSD) provides dense token-level supervision without a second model: one network acts as teacher with the reference solution and as student with only the problem. We identify a specific failure mode of this recipe. During training, student token entropy rises past the teacher's and remains...
Yi-Fei Liu, Ming-Hao Fang, Xin-Yu Gu et al.· 0 citations
A conditional transport model that represents cells as unordered sets and predicts treated populations without cell-level matching that enables perturbation-specific prediction from unpaired populations and supports experimental prioritization is presented.
Shui-Zhou Chen, Lang Yu, Xu Lin et al.· bioRxiv· 0 citations
SciReasoner is introduced, a multimodal scientific foundation model for native structural reasoning across proteins, small molecules and inorganic crystals that connects accurate prediction with interpretable scientific inference.
Chen Tang, Yizhou Wang, Jianyu Wu et al.· 1 citation
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