The Testability Gap: A Pre-Registered Census of Naming versus Implementation in 59 Cognitive-Architecture LLM Systems

Jia Chen, Ying Xu, Ni Zhang, Siyuan Zhang

A companion line of work audited individual cognitive-architecture LLM systems in depth and found that module names and module computations are empirically independent claims. This paper asks how far that gap extends across the system class. We pre-registered a census protocol (sample frame, metrics, and acceptance criteria frozen before any sample was downloaded) and surveyed the 59 most-starred Python repositories declaring cognitive architecture on GitHub, plus five classic architectures as a historical control. We measure a testability gap. First, an auditability gap: 22% of repositories document no code reference whatsoever. Second, an integrity gap: 42% of repositories have at least one dangling reference, and within that subgroup the median fraction is 8%, the top quartile 19.6%, and the maximum 79.5%; one 148-star system documents paths of which a majority do not exist. Three of four pre-registered hypotheses were falsified: the integrity gap is not universal (58% of repositories either offer no checkable references or offer references that all resolve); claim density does not predict dangling rate (rho = -0.10); and the classical-architecture control does not…