Generative AI increasingly supports scientific inference, from protein structure prediction to weather forecasting. Yet its distinctive failure mode, hallucination, raises epistemic alarm bells. I argue that this failure mode can be addressed by shifting from data-centric to phenomenon-centric assessment. Through case studies of AlphaFold and GenCast, I show how scientific workflows discipline generative models through theory-guided training and confidence-based error screening. These strategies convert hallucination from an unmanageable epistemic threat into bounded risk. When embedded in such workflows, generative models support reliable inference despite opacity, provided they operate in theoretically mature domains.

翻译：生成式AI日益支持从蛋白质结构预测到天气预报等科学推断。然而其独特的失效模式——幻觉——引发了认识论层面的警示。我认为，这种失效模式可通过从以数据为中心转向以现象为中心的评估来解决。通过对AlphaFold和GenCast的案例研究，我展示了科学工作流如何通过理论指导的训练和基于置信度的误差筛选来约束生成模型。这些策略将幻觉从不可控的认识论威胁转化为有限风险。当嵌入此类工作流时，只要在理论成熟的领域运行，生成模型就能在保持不透明性的同时支持可靠推断。