What question did this study set out to answer?

This research aims to establish a framework for maintaining human meaning in AI systems through the Dynamic Equilibrium equation.

May 20, 2026Open Access

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

Key Points

This research aims to establish a framework for maintaining human meaning in AI systems through the Dynamic Equilibrium equation.
Formalized the Dynamic Equilibrium equation for modeling AI optimization and human struggle.
Utilized 106 adversarial stress scenarios to validate the framework.
Implemented interventions like Logit Bias Masking and Novelty Harvest metrics.
Achieved an 81% reduction in AI dominance events when human struggle is preserved above critical thresholds.

Abstract

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

Read Full Paperexternally

Mark Helpful

Bookmark

Relay

View Full Paper