GoKawiilThis 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.
Cross-Model Void Convergence: GPT-5.2 and Claude Opus 4.6 Deterministic Silence
Why This Matters
This research highlights a significant behavioral convergence between leading language models, GPT-5.2 and Claude Opus 4.6, in their consistent response to null prompts, revealing shared boundaries in their semantic understanding. Such findings are crucial for advancing model interpretability, safety, and alignment, ensuring more predictable AI behavior across different systems. This understanding can guide the development of more robust and ethically aligned AI applications in the tech industry and for consumers.
Key Takeaways
- Models exhibit shared boundaries in null prompt responses.
- Behavioral convergence is reproducible and boundary-expanding.
- Findings enhance interpretability and safety in AI systems.
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