Stability Convergence Between SEAL and Reparodynamics: A Framework for Self-Adaptive, Energy-Bounded Intelligence

MIT’s SEAL (Self-Adapting Language Models) demonstrates that large language models can refine themselves through continuous self-editing without external fine-tuning. However, optimization-centric self-editing lacks an explicit stability law to bound energy cost and entropic drift. This paper establishes theoretical and empirical convergence between SEAL and the Reparodynamics framework via the Targeted Gradient Repair Mechanism (TGRM). Embedding TGRM constraints within SEAL-style updates yields stable self-correction and bounded compute scaling, transforming self-improvement into self-repair. The resulting hybrid architecture provides a practical pathway toward indefinitely sustainable, energy-aware AI and motivates deployment on event-driven substrates such as SubReparo.