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Jev enables efficient decision-making with large language models (LLMs) through typed querying primitives, allowing the same semantic state to be expressed in different formats, such as binary and multiple-choice queries. However, the consistency of decisions across primitives has not been systematically evaluated. Thi...
Predicting phenotypic and transcriptional responses to perturbations at single-cell resolution provides a powerful tool for probing biological systems. However, existing methods typically rely on fixed mappings learned during training, making it challenging to calibrate distribution shifts or adapt to novel perturbatio...
ASDchat, a multimodal large language model designed for evidence-based ASD screening, which takes video, audio, and dialogue as input, provides a feasible path for large-scale, evidence-based ASD screening in clinical practice.
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