用大模型自动搜寻疾病预测模型，省去人工调参，效果媲美专家团队。
Prospective multi-pathogen disease forecasting using autonomous LLM-guided tree search
- 用大模型引导树搜索，自动生成、评估并优化预测代码。
- 在2025-2026年美国呼吸系统疾病季中，对流感、新冠、RSV预测均超越或持平CDC基准。
- 可在数据少的冷启动场景下工作，适合快速部署新病种预测。
传染病概率预测对公共健康至关重要，但依赖专家团队手动构建模型，效率低下且难以扩展至细粒度地理区域或新发病原体。本文提出一种自主系统，利用大语言模型（LLM）引导的树搜索，迭代生成、评估并优化可执行的预测软件。在2025-2026年美国呼吸系统疾病季节的全前瞻性实时评估中，该系统自主发现了针对流感、新冠和呼吸道合胞病毒（RSV）的方法多样化的模型。聚合这些机器生成的模型形成集成系统，在未见数据上持续匹配或优于人类精心设计的美国疾控中心（CDC）基准集成。系统成功应对了RSV数据稀缺的“冷启动”挑战。受控回溯消融实验表明，优化对数尺度距离度量可防止奖励欺骗，而自动化裁判机制确保了模型结构与复杂科学理论的一致性。该框架通过将流行病学理论自动转化为准确、透明的代码，突破建模人力瓶颈，实现前所未有的大规模专家级疾病预测快速部署。
原文摘要 · Abstract (English)
Probabilistic forecasting of infectious diseases is crucial for public health but relies on labor-intensive manual model curation by expert modeling teams. This bespoke development bottlenecks scalability to granular geographic resolutions or emerging pathogens. Here, we present an autonomous system using Large Language Model (LLM)-guided tree search to iteratively generate, evaluate, and optimize executable forecasting software. In a fully prospective, real-time evaluation during the 2025-2026 US respiratory season, the system autonomously discovered methodologically diverse models for influenza, COVID-19, and respiratory syncytial virus (RSV). Aggregating these machine-generated models yielded an ensemble that consistently matched or outperformed the gold-standard, human-curated Centers for Disease Control and Prevention (CDC) hub ensembles out-of-sample. The system successfully navigated data-scarce "cold start" scenarios for RSV. Moreover, controlled retrospective ablations revealed that optimizing log-scale distance metrics prevents reward hacking, while an automated judge-in-the-loop ensures structural fidelity to complex scientific theories. By autonomously translating epidemiological theory into accurate, transparent code, this framework overcomes the modeling labor bottleneck, enabling rapid deployment of expert-level disease forecasting at unprecedented scales.
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