Resonant Semantic Archictures - LLM

This documents presents , a visionary framework addressing the limitations of modern AI through Semantic Pressure (SP)—a novel metric for quantifying and mitigating hallucinations in large language models (LLMs). Bridging the Meta Thesis (structural openness), Anthony Thesis (intrinsic-altruistic cognition), and Adrian Thesis (systemic equilibrium), the project proposes an architecture for evolving error-prone AI into resonant systems that align with ethical, contextual, and evolutionary imperatives. Key Concepts: Semantic Pressure (SP): A dynamic measure of semantic complexity, balancing token entropy, sentiment load, and context divergence to modulate LLM outputs. Three Theses Synthesis: Unifies open inquiry (Meta), altruistic intent (Anthony), and attractor-based stability (Adrian) into a cohesive design philosophy. Resonant Systems: AI architectures that self-regulate via ethical resonance, contextual grounding, and emergent coherence.