BioDyad：同步生物医学发现与机器学习工程
BioDyad: Synchronize Biomedical Discovery and Machine Learning Engineering
AI总结：
BioDyad通过蒙特卡洛图搜索中的科学和工程双层级，同步生物医学发现与机器学习工程，在BioXArena基准上取得最优性能，实现外部知识整合到可执行程序。
AI中文摘要：
智能体生物医学机器学习（ML）借鉴了生物医学证据获取和可执行程序搜索方面的互补进展。现有系统连接了这些能力的某些方面，但在整个程序搜索过程中协调它们仍然具有挑战性。新证据必须指导候选程序的构建，执行结果必须为后续发现和重用提供信息，验证需求必须适应搜索预算。我们引入了BioDyad，它通过蒙特卡洛图搜索中的两个层级将生物医学发现与ML工程耦合。其科学层级将先前的生物医学指导与迭代发现相结合，然后将生物医学计划与执行结果在内存中关联，以便跨候选程序重用。其工程层级将候选程序从冒烟执行，经过训练/验证评估，推进到全数据重训练。我们在76任务BioXArena基准上，在每任务两小时的预算下，使用三个匹配的LLM后端评估了BioDyad。在每个后端下，它在四种智能体方法和一种一次性基线中取得了最高的惩罚性全任务得分和任务成功率。这些结果支持协调生物医学发现与ML工程，以将外部知识整合到跨异构生物医学任务的可执行程序中。
英文摘要：
Agentic biomedical machine learning (ML) draws on complementary advances in biomedical evidence acquisition and executable program search. Existing systems connect aspects of these capabilities, but coordinating them throughout program search remains challenging. New evidence must guide candidate construction, execution outcomes must inform subsequent discovery and reuse, and validation demands must fit the search budget. We introduce BioDyad, which couples biomedical discovery and ML engineering through two hierarchies within Monte Carlo graph search. Its scientific hierarchy combines prior biomedical guidance with iterative discovery, then links biomedical plans to execution outcomes in memory for reuse across candidates. Its engineering hierarchy moves candidate programs from smoke execution, through train/validation evaluation, to full-data retraining. We evaluate BioDyad on the 76-task BioXArena benchmark under a two-hour per-task budget with three matched LLM backends. It achieves the highest penalized all-task score and task success rate among four agent methods and a one-shot baseline under each backend. These results support coordinating biomedical discovery and ML engineering to integrate external knowledge into executable programs across heterogeneous biomedical tasks.
