发表机构
The Tesseract Academy（泰瑟拉克特学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过卫星目录管道案例揭示共模故障使独立验证失效，冗余仅验证实现而非含义，导致错误数据发布，强调需从源头文档和对象历史核查数据含义。
AI 中文摘要
数据管道的一种常见保护措施是冗余计算：通过基于不同技术构建的两条路径推导每个发布数字，并在它们不一致时拒绝退出。我们报告了这样一个门在一个跨目录完整性研究中失效的情况，该研究涉及两个地球轨道物体的开放登记册。一个门将基于集合的Python路径与对生成的RDF图的SPARQL查询进行比较，在七个计数上打印了“所有交叉检查一致”。其中三个是错误的，一个高估了四倍以上（932对220）。两条路径都导入了相同的常量，这些常量编码了对来源状态词汇的误读，因此错误是共模的，门无法察觉。我们给出了机制、一个协调每个数字的对象级账本，以及三个追溯到来源文档的检查，这些检查在缺陷代码及其修正上进行了测量。然后，我们针对每个物体的相位历史检查了该修正，该历史保存在管道从未读取的源文件中。修正也是错误的：其261个不一致中有42个是伪影，我们的三个检查都没有标记它们。最后，在一个受控复制中，使用三个固定模型并禁用工具，按请求生成的75条独立检查路径中有72条计算了有缺陷的计数，即使提示携带了来源自身对代码的定义，30条中有29条也是如此。证据是一个管道和一个缺陷家族。在其中，冗余验证了实现，而到达发布的错误是含义错误。
英文摘要
A common safeguard for a data pipeline is redundant computation: derive each published number by two routes built on different technology and refuse to exit when they disagree. We report one such gate failing, in a cross-catalogue integrity study of two open registers of Earth-orbiting objects. A gate comparing a set-based Python path with SPARQL queries over the emitted RDF graph printed ALL CROSS-CHECKS AGREE on seven counts. Three were wrong, one overstated more than fourfold (932 against 220). Both paths imported the same constants, which encoded a misreading of the source's status vocabulary, so the error was common-mode and the gate could not see it. We give the mechanism, an object-level ledger reconciling every figure, and three checks that go back to the source's documentation, measured on the defective code and on its correction. We then checked that correction against each object's phase history, held in a source file the pipeline never read. The correction was also wrong: 42 of its 261 disagreements are artefacts, and none of our three checks flagged them. Finally, in a controlled replication with three pinned models and tools disabled, 72 of 75 paths generated on request as independent checks computed the defective count, 29 of 30 even when the prompt carried the source's own definitions of the codes. The evidence is one pipeline and one defect family. Within it, redundancy verified implementation, and the errors that reached publication were errors of meaning.
CommentsAccepted at the AI for Science workshop (NeurIPS 2026). Code, prompts, raw model outputs and per-trial records: https://github.com/fabio-rovai/space-object-register-ontology (paper/gates/), archived at https://doi.org/10.5281/zenodo.22002834
