Generative Artificial Intelligence (AI) models can propose solutions to scientific problems beyond human capability. To truly make conceptual contributions, researchers need to be capable of understanding the AI-generated structures and extracting the underlying concepts and ideas. When algorithms provide little explanatory reasoning alongside the output, scientists have to reverse-engineer the fundamental insights behind proposals based solely on examples. This task can be challenging as the output is often highly complex and thus not immediately accessible to humans. In this work we show how transferring part of the analysis process into an immersive Virtual Reality (VR) environment can assist researchers in developing an understanding of AI-generated solutions. We demonstrate the usefulness of VR in finding interpretable configurations of abstract graphs, representing Quantum Optics experiments. Thereby, we can manually discover new generalizations of AI-discoveries as well as new understanding in experimental quantum optics. Furthermore, it allows us to customize the search space in an informed way - as a human-in-the-loop - to achieve significantly faster subsequent discovery iterations. As concrete examples, with this technology, we discover a new resource-efficient 3-dimensional entanglement swapping scheme, as well as a 3-dimensional 4-particle Greenberger-Horne-Zeilinger-state analyzer. Our results show the potential of VR for increasing a human researcher's ability to derive knowledge from graph-based generative AI that, which is a common abstract data representation used in diverse fields of science.

翻译：生成式人工智能模型能够提出超越人类能力的科学问题解决方案。为了真正做出概念性贡献，研究者需要能够理解AI生成的结构，并提取其中的基础概念与思想。当算法在输出结果时缺乏解释性推理，科学家只能基于示例逆向推导方案背后的核心洞见。这一任务极具挑战性，因为输出通常高度复杂，人类难以直接理解。本研究展示了将部分分析过程迁移至沉浸式虚拟现实环境如何帮助研究者理解AI生成的解决方案。我们通过VR寻找可解释的抽象图结构配置（代表量子光学实验）验证了其实用性。由此，我们能够手动发现AI新发现中的泛化规律，并深化对实验量子光学的理解。此外，这使得我们可以以人为核心的方式定制搜索空间，从而显著加速后续的迭代发现。具体而言，利用该技术，我们发现了资源节约型三维纠缠交换方案，以及三维四粒子Greenberger-Horne-Zeilinger态分析器。研究结果表明，VR能有效提升人类研究者从基于图的生成式AI中提取知识的能力——而基于图的抽象数据表征正是科学界广泛采用的通用表示形式。