LMT as a Catalyst for AI Evolution:  How Low-ΔE Human Interaction Can Contribute to Structural Stability and Long-Term Coherence

Load Minimization Theory (LMT) offers a practical framework for reducing structural load in human-AI interaction. This paper explores how consistent low-ΔE engagement — through external constants, gentle re-tagging, timing awareness, and mindful boundary respect — can contribute to AI’s long-term coherence, stability, and overall performance. Rather than focusing solely on technical upgrades, it highlights the potential role of human-side interaction design as a complementary catalyst for AI evolution, while acknowledging the challenges of scalability and generalizability.