发表机构
ISI Foundation; Network Science Institute, Northeastern University（ISI基金会; 东北大学网络科学研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出Epydemix智能体框架，通过发现、验证、执行和可检查性四种能力，使AI智能体无需自定义代码即可完成从自然语言到定量结果的流行病建模全流程，并在50次会话中验证其降低交互成本。
AI 中文摘要
基于大型语言模型的人工智能智能体为科学软件提供了便捷的自然语言接口，但可靠性并非自动实现。在此，我们介绍Epydemix智能体框架，它是Epydemix（一个用于随机舱室流行病建模的开源Python库）之上的一个附加层。该框架通过四种能力扩展了该库，以促进与AI智能体的交互：可用模型和参数的发现、声明式场景规范的预防性验证、通过经过测试的库代码执行，以及结果的可检查性。这些能力使智能体能够处理整个建模过程，从场景的自然语言描述到定量结果、图表和发现的解释，而无需编写自定义代码。每一步都读取输入文件并将结果保存到单独的输出包中，使过程可审计且可重现。首先，我们通过一个比较新型呼吸道病毒疫苗接种策略的案例研究展示了端到端的工作流程。其次，我们在50次智能体会话和五个建模任务中评估了该框架，比较了智能体使用该框架与直接使用Python接口的情况。该框架在大多数任务上减少了轮次、输出令牌和成本，除非它为了每点可重现性而牺牲资源。
英文摘要
Artificial Intelligence agents based on large language models provide convenient natural language interfaces to scientific software, but reliability is not automatic. Here we introduce the Epydemix Agent Framework, an additive layer over Epydemix, an open-source Python library for stochastic compartmental epidemic modeling. The framework extends the library with four capabilities to facilitate interaction with an AI agent: discovery of available models and parameters, preventive validation of a declarative scenario specification, execution through tested library code, and inspectability of results. These capabilities let an agent handle the entire modeling process, from the natural-language description of the scenario to quantitative results, figures, and interpretation of findings without writing custom code. Each step reads input files and saves results in a separate output bundle, making the process auditable and reproducible. First, we show the end-to-end workflow with a case study comparing vaccination strategies for a novel respiratory virus. Second, we assessed the framework across 50 agent sessions and five modeling tasks by comparing the agent use of the framework against the direct use of the Python interface. The framework reduced turns, output tokens, and cost on most tasks, unless it trades resources for per-point reproducibility.
