ETERNAL 3.0: A Resource-Governed and Cognitively Adaptive Architecture for AI Safety

ETERNAL 3.0 introduces a novel AI governance architecture that reframes safety as a dynamic systems problem rather than a static alignment constraint. The framework integrates five core innovations: Compute as a Governed Resource (CGR), Progressive Cognitive Scaffolding (Evol 2.0), Dual-Track Output Architecture, Adversarial-Aware Interaction Mode, and Multi-Dimensional Drift Modeling. Unlike traditional approaches such as RLHF and constitutional prompting, ETERNAL 3.0 dynamically allocates computational resources based on intent risk and epistemic value, while simultaneously shaping user cognition through adaptive interaction loops. The system introduces transparent audit layers and vector-based drift detection, enabling improved robustness against adversarial prompting and semantic drift. This work proposes a meta-governance layer applicable to large language models and broader AI systems, with implications for safety, efficiency, and long-term human-AI co-evolution.