EpiGraph: Meta-Graph Engineering for Agentic Scientific Reasoning

Agentic scientific reasoning is attracting growing attention because it enables agent systems to support scientific decisions and discoveries. Despite rapid progress with increasingly capable large language models, task requirements, intermediate actions, expected observations, and supporting evidence are essential for building capable agent systems in this domain, yet they are often not maintained as a coherent execution structure. This makes execution validity a general and central challenge across scientific tasks, even when the underlying domain knowledge and tools differ. We name this engineering perspective Meta-Graph Engineering and introduce \ourmethod, a method that represents scientific execution as a structured graph maintained throughout the task. First, graph operators construct, expand, prune, admit, close, and revise the execution graph; second, typed edges determine whether actions are licensed, what they are expected to produce, and whether their saved results satisfy the corresponding obligations; third, this shared execution structure provides a reusable way to organize agentic scientific reasoning across tasks and systems. We evaluate \ourmethod across scientific, mathematical, and biomedical benchmarks with multiple agent configurations and find consistent outcome improvements. Notably, it surpasses biomedical agent systems designed by human experts on BiomniBench without supplying biological prior knowledge or task-specific biological interpretations during execution. These results suggest that \ourmethod provides a general execution layer for making agentic scientific reasoning more reliable, inspectable, and reusable.