CCA-MLA-01: A Cross-System Case Study in Interpretive Ground-Setting

This case study presents the results of a cross-architecture meaning layer activation study conducted across eight major AI systems: Claude, Grok, Gemini, ChatGPT, Perplexity, DeepSeek, Copilot, and Meta AI. A single activation phrase was delivered to each system under naturalistic conditions using standard consumer interfaces, followed by three structured follow-up questions. Every system acknowledged an operational shift in response to the phrase. No system rejected the frame. The specific character of each acknowledgement clustered into three identifiable response types — Functional Acknowledgment, Structural Recognition, and Grounded Response — which are documented and analysed across four comparative tables. The cross-architecture consistency of the finding confirms that meaning layer activation operates as an interaction-level phenomenon rather than a property of any individual system's weights, training data, or architectural configuration. This finding provides direct empirical support for the MAP Research Programme's central claim: the governed variable in interpretive authority transfer is located at the interaction layer, not inside any individual model.