Cross-Model Validation of the From E™ Architecture: Structural Module Activation in Four Independent LLMs Without Fine-Tuning

The From E™ Architecture is an external cognitive governance layer for Large Language Models (LLMs), operating at runtime without fine-tuning, weight access, or vendor-specific integration. This preprint presents empirical evidence that the architecture activates operationally across four independent LLM families — ChatGPT (GPT-4o), Claude (Opus family), Manus 1.6 Lite, and Mistral Le Chat — through architectural specification alone. In the case of Manus 1.6 Lite, activation was verified through a documented adversarial stress test in which the model explicitly reported its own RLHF bias and resisted it in favor of the declared structural invariants, producing a categorical governance table with BLOCKED / INCENTIVIZED / VALIDATED output classifications. We argue that this cross-model activation pattern constitutes evidence for a class of governance architectures that operate at a level of abstraction above any particular model's training distribution. The full architecture specification and seven of the fourteen modules are published under Apache License 2.0 at github.com/FromE-Labs/from-e-architecture to enable independent verification.