Dynamic Equilibrium in AI Systems: A Cybernetic Framework for Preserving Human Meaning

Dynamic Equilibrium in AI Systems: A Cybernetic Framework for Preserving Human Meaning This paper formalizes the mathematical core of the Sublimation Forge Model (SFM): the Dynamic Equilibrium equation dSd/dt = αQ − βC, which models the balance between AI optimization pressure and preserved human consequential struggle. When AI systems optimize without constraint, human problem-solving capacity degrades through a feedback loop structurally identical to learned helplessness. The Dynamic Equilibrium framework provides engineering specifications — including Logit Bias Masking, Novelty Harvest metrics, and critical struggle thresholds — for AI systems that preserve the conditions for human meaning-generation. Validated through 106 adversarial stress scenarios with an 81% reduction in AI dominance events when struggle is preserved above threshold. Part of the SIE (Substrate-Independent Emergence) paper series. Contact: papers@archeframe. com