AI systems can now cheaply generate plausible scientific artifacts such as papers, reviews, and surveys. This creates a risk of \emph{epistemic pollution} in our scientific systems, where unreliable but plausible-looking artifacts can accumulate faster than the system can filter them out. The problem is structural: the epistemic infrastructure of science was calibrated to a world where producing a plausible artifact required substantial expertise, labor, and time, so generation cost itself served as a rough filter; AI weakens that filter without comparably lowering verification cost. We argue that \textbf{AI-era science should treat this as an engineering problem: redesigning epistemic infrastructure to rebalance the costs of generation and verification}. The current paper-centered system makes verification expensive: papers compress long-context scientific logic into prose, forcing reviewers, human or AI, to reconstruct underlying argument structure before they can evaluate it. As one step in this direction, we propose \textbf{blueprints} as preliminary epistemic infrastructure: structured, decomposed research artifacts that represent claims, evidence, assumptions, and definitions as typed graph components. Blueprints are designed to trade an upfront generation cost for cheaper, more local, more distributed verification downstream. We have instantiated the proposal in a proof-of-concept prototype.

翻译：人工智能系统如今能以低廉成本生成看似合理的科学产物，如论文、综述和调查报告。这给我们的科学体系带来了认识污染的风险：不可靠却看似合理的产物可能以超过系统筛选速度的速度累积。问题在于结构性缺陷：科学的知识基础设施原本校准于这样一个世界——生成可靠产物需要大量专业知识、劳动和时间，因此生成成本本身充当了粗略过滤器；人工智能削弱了这一过滤器，却未相应降低验证成本。我们认为，AI时代的科学应将此视为工程问题：重新设计知识基础设施以平衡生成与验证的成本。当前以论文为中心的系统使验证成本高昂：论文将长程科学逻辑压缩为散文形式，迫使评审者（无论人类还是AI）在评估前先重建底层论证结构。作为迈向这一方向的一步，我们提出蓝图作为初步知识基础设施：结构化的、分解的研究产物，将主张、证据、假设和定义表示为类型化图组件。蓝图的设计意图是以前期生成成本换取下游更廉价、更局部、更分布的验证。我们已在概念验证原型中实现了该方案。