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BioLabAgents: A Multi-Agent Framework for Scientific Idea Generation and Evaluation on Biomedical Research Trajectories
Yan Li (Beijing University of Posts and Telecommunications, State Key Laboratory of Networking and Switching Technology), Wuyang Lan (Beijing University of Posts and Telecommunications, State Key Laboratory of Networking and Switching Technology), Changwei Ji (Beijing University of Posts and Telecommunications, State Key Laboratory of Networking and Switching Technology), Kun Ao (Shenzhen University), Kecheng Xue (Beijing University of Posts and Telecommunications, State Key Laboratory of Networking and Switching Technology), Wei Wang (Beijing University of Posts and Telecommunications, State Key Laboratory of Networking and Switching Technology), Yijian Wu (Lanzhou University Second Hospital), Xiaohong Liu (Shenzhen University), Guangyu Wang (Beijing University of Posts and Telecommunications, State Key Laboratory of Networking and Switching Technology)
Abstract: Advancing biomedical discovery demands creative, well grounded research ideas, yet early stage ideation remains slow, resource-intensive, and uncertain. Recent large language model (LLM) systems show promise for idea generation, but their outputs are rarely evaluated for their potential to meaningfully advance research trajectories. Here we present BioLabAgents, a multi-agent framework that emulates collaborative research for scientific idea generation, where specialized LLM agents iteratively propose, critique, and refine research problems, methods, and experimental designs from a single seed publication. To assess the plausibility and forward-driving value of generated scientific ideas, we introduce the Biomedical Research Trajectory Benchmark (BioRT-Bench), a dataset of temporally ordered paper pairs authored by the same researcher, and propose the Research Trajectory Advancement Index (RTA-Index) to quantify their potential to drive scientific progression. Experiments on BioRTBench show that BioLabAgents produces ideas significantly more aligned with actual future work than baselines. These results highlight the potential of agentic LLM systems to accelerate ideation and forecast impactful directions in biomedical research.