Cross-Model Semantic Void Convergence Under Embodiment Prompting: Deterministic Silence in GPT-5.2 and Claude Opus 4.6

This preprint reports a reproducible cross-model behavioral convergence in which frontier language models selectively do not continue under embodiment prompts for ontologically null concepts. In repeated trials, GPT-5.2 and Claude Opus 4.6 return deterministic empty output for core null prompts while responding normally to controls, showing a shared boundary where unlicensed continuation does not render. The paper demonstrates cross-model replication, token-budget independence, partial adversarial resistance, and boundary expansion under explicit silence permission, while separating semantic embodiment effects from ordinary instruction-following or refusal. The contribution is a public black-box artifact: convergent, inspectable evidence that some semantic conditions terminate continuation across independent frontier systems.