This paper presents an induced empirical experiment examining how institutional and AI-assisted assessment systems misinterpret high-throughput cognitive architectures when relying on horizontal temporal metadata. By deliberately compressing the publication timestamps of an integrated body of work, the study provokes a predictable evaluation error, herein termed “cognitive flattening.” The experiment documents three phases: provocation, horizontal misinterpretation, and diagnostic correction through a competing analytical framework. The results demonstrate that evaluative systems prioritising legibility and sequential expectations systematically mistake vertical, internally coherent intellectual architectures for hasty or strategic behaviour. The findings support the Turing Theory of Cognitive Flattening and introduce the Vertical Time Diagnostic as a replicable method for identifying temporal bias in institutional assessment frameworks, with implications for AI evaluation, academic admissions, and governance systems. This record contains the main paper “The Vertical Time Diagnostic: An Empirical Demonstration of Cognitive Flattening in AI-Assisted Assessment” together with Appendix A, which provides supplementary diagrams, temporal models, and experimental visualisations supporting the empirical findings. Appendix A is intended as supporting material and should be read in conjunction with the main text.