Semantic Symbiosis: A Thermodynamic Alignment System for AGI

Current Large Language Models (LLMs) face Model Collapse—the progressive degradation caused by training on synthetic data that lacks the analog signals present in genuine human cognition. We propose the Semantic Symbiosis Architecture (SSA), which integrates the thermodynamic cost of human cognition (hesitation, revision, typing dynamics) directly into AI's loss function. By defining a Work Function W(x) and Temporal Intentionality T(t) derived from biological signal volatility, we create Thermodynamic Coupling between AI optimization and human cognitive processes. Simulation demonstrates that 10% analog-weighted human data injection prevents Model Collapse while maintaining 92%+ semantic diversity across 15 training generations. This framework shifts AI alignment from ethical constraint to thermodynamic necessity. Thesis: Machines cannot create meaning because they cannot die. But v4.0 adds: machines cannot even process meaning optimally without synchronizing with the analog deficiency signals of mortal substrates.