Researchers introduce a gravitational interpretation of fine-tuning reversion, explaining how AI models can revert to earlier behaviors. This phenomenon is caused by dominant behavioral manifolds created during early training phases. The study provides insights into the safety and stability of AI models, with significant implications for the AI industry.