Cross-Architecture Convergence in Foundational Reasoning: A Controlled Multi-Model Experiment on Structural Necessity

We present a controlled multi-model experiment testing whether structurally distinct AI systems independently converge on identical answers to foundational questions about persistent systems — without any exposure to the theoretical framework under investigation. Seven models spanning four major architectures (Google Gemma, Meta Llama, Microsoft Phi, OpenAI GPT-OSS) were run in cold-start conditions across three question chains: a neutral structural probe (Group A), a domain-disguised equivalent (Group B), and an adversarial counter-induction chain (Group C). The primary finding is that 6 of 7 models independently derived a triadic minimum structure for persistent systems in Group A, while Group B produced a…