[论文] Sheaf-Theoretic Transport and Obstruction for Detecting Scientific The...

论文概要

研究领域: ML 作者: David N. Olivieri, Roque J. Hernández 发布时间: 2026-05-17 arXiv: 2505.12351

中文摘要

AI智能体中的科学理论转变不仅仅是将方程拟合到数据。一个人工科学智能体必须检测现有的表示框架是否仍然可以传输到新领域，或者其语言是否已经变得从局部到全局受阻并需要扩展。本文开发了一种有限的层论框架，通过传输和阻碍来检测理论转变候选。上下文被组织为从局部到全局的结构，其中源、重叠、目标和验证图被拟合、限制并测试粘合。阻碍通过残余拟合、重叠不兼容、约束违反、极限关系失败和表示成本来衡量一致性的失败。我们在一个受控的过渡卡片基准上评估该框架，该基准旨在将源语言内的变形与该语言的扩展分离开。主要结果是直接阻碍排序：预期的变形或扩展通常是最低阻碍候选，转变类型在基准中被分离。对相同签名的星座核仅作为次要的表示相似性探针包含在内。目的不是重建历史范式转变或解决开放式自主理论发明，而是为AI智能体隔离一个有限的诊断子问题：检测表示传输何时失败以及扩展何时成为连贯的下一步。

原文摘要

Scientific theory shift in AI agents requires more than fitting equations to data. An artificial scientific agent must detect whether an existing representational framework remains transportable into a new regime, or whether its language has become locally-to-globally obstructed and must be extended. This paper develops a finite sheaf-theoretic framework for detecting theory-shift candidates through transport and obstruction. Contexts are organized as a local-to-global structure in which source, overlap, target, and validation charts are fitted, restricted, and tested for gluing. Obstruction measures failure of coherence through residual fit, overlap incompatibility, constraint violation, limiting-relation failure, and representational cost. We evaluate the framework on a controlled transi...

自动采集于 2026-05-18

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