12 papers
Entry-level guide to the use of large language models for medical research
Qiao Jin, Nicholas Wan, Robert Leaman +20
Frontier large language models (LLMs), such as GPT-5, Claude 4.5, Gemini 3, Llama 4, and DeepSeek-R1, represent a transformative class of AI tools capable of revolutionizing variou…
Adaptation of Agentic AI: A Survey of Post-Training, Memory, and Skills
Pengcheng Jiang, Jiacheng Lin, Zhiyi Shi +31
Large language model (LLM) agents are moving beyond prompting alone. ChatGPT marked the rise of general-purpose LLM assistants, DeepSeek showed that on-policy reinforcement learnin…
DeepEvidence: Empowering Biomedical Discovery with Deep Knowledge Graph Research
Zifeng Wang, Zheng Chen, Ziwei Yang +5
Biomedical knowledge graphs (KGs) encode vast, heterogeneous information spanning literature, genes, pathways, drugs, diseases, and clinical trials, but leveraging them collectivel…
Developing Large Language Models for Clinical Research Using One Million Clinical Trials
Zifeng Wang, Jiacheng Lin, Qiao Jin +5
Developing artificial intelligence (AI) for clinical research requires a comprehensive data foundation that supports model training and rigorous evaluation. Here, we introduce Tria…
s3: You Don't Need That Much Data to Train a Search Agent via RL
Pengcheng Jiang, Xueqiang Xu, Jiacheng Lin +4
Retrieval-augmented generation (RAG) systems empower large language models (LLMs) to access external knowledge during inference. Recent advances have enabled LLMs to act as search…
BioDSA-1K: Benchmarking Data Science Agents for Biomedical Research
Zifeng Wang, Benjamin Danek, Jimeng Sun
Validating scientific hypotheses is a central challenge in biomedical research, and remains difficult for artificial intelligence (AI) agents due to the complexity of real-world da…