arXiv:2610.34573 · v1

Software Engineering for AI-Driven Science

首次提交 2026-10-26 05:14

版本更新 2026-10-26 05:14

雷达观测 2026-10-29 00:36

摘要

AI has made significant contributions to scientific discovery. Autonomous agents can now independently analyse complex problems, design experiments, write and execute code, analyse findings, and derive scientific conclusions. Nevertheless, errors in AI-generated code, experimental procedures, or evidence interpretation can lead to invalid research conclusions, undermining the reliability and reproducibility of automated scientific discovery. In this vision paper, we propose Software Engineering for AI-Driven Science (SE4AI4Sci) as an emerging research area for addressing this challenge. As an organising framework, we introduce the Scientific Assurance Chain, connecting intent, experimental design, implementation, execution, evidence, and claim. Through six documented failures in recent AI-for-science systems, we show where this chain can break, where existing assurance mechanisms fall short, and what new research challenges follow. We also outline a research agenda for adapting and extending software engineering techniques across the chain.

作者备注：5 pages, 1 figure