Personalized AI Practice Replicates Learning Rate Regularity at Scale

This ArXiv feed (multiple CS/Cy papers) collects recent work on personalized learning, AI ethics and governance, model fine‑tuning, digital inclusiveness, and empirical NLP/ML applications. Notable items: (1) large-scale evidence (1.8M interactions, 366k filtered) that automated KC generation preserves learning-rate regularity and public code/data; (2) a Deception Research Levels (DRL) framework proposing biosafety‑style risk tiers and safeguards for deceptive AI research; (3) BLK-Assist, a modular artist-led diffusion fine‑tuning pipeline (LoRA, LayerDiffuse, Real-ESRGAN) demonstrating style‑