4 papers
PertMind: Eliciting Emergent Biological Reasoning in LLM via Reinforcement Learning on Cellular Perturbation Data
Zhenchao Tang, Xiaogang Xu, Tianxu Lv +8
Large language models can describe mechanisms, yet scalable post-training still depends on costly, manually curated biological reasoning traces. Here we show that cellular perturba…
Branch-JEPA: Finite-Support Predictive Distributions for JEPA World Models
Zhi Song, Ximing Xing, Zhenchao Tang +9
Joint-embedding predictive architectures (JEPAs) learn dynamics by predicting future observations in representation space. Yet most JEPA world models return one latent successor, e…
Multimodal Mixture-of-Experts with Retrieval Augmentation for Protein Active Site Identification
Jiayang Wu, Jiale Zhou, Rubo Wang +5
Accurate identification of protein active sites at the residue level is crucial for understanding protein function and advancing drug discovery. However, current methods face two c…
Aligning LLMs with Biomedical Knowledge using Balanced Fine-Tuning
Zhenchao Tang, Fang Wang, Haohuai He +12
Engineering LLMs to accelerate life sciences research requires a robust alignment with biomedical knowledge. We observe that biomedical text exhibits a fundamentally different unce…