This work presents a complete framework for recursive self-improvement in language models, validated through extensive experimentation on an 8-billion parameter model. The core contribution is the discovery that the RSI (Recursive Self-Improvement) ceiling—previously observed at 3-5 iterations—is not a fundamental limit but a tokenization bottleneck. By identifying high-stress token boundaries using a novel entropy-attention discontinuity metric and expanding the vocabulary with merged tokens, we create representational headroom that enables continued self-improvement. Key validated results:- CF-HoT behavioral probe: 80× separation ratio, 97.2% accuracy- Dense training pipeline: 68% density improvement, 57% token reduction- Loop 4 tokenization co-evolution: 9.87% token reduction across 30 merge candidates- RSI ceiling breakthrough: 10/10 successful iterations (previous ceiling: 3-5) The framework comprises four interconnected optimization loops:1. Inference-time behavioral control through hidden state probing and decode-time intervention2. Density optimization through SFT → DPO → PPO training3. Bounded recursive self-improvement with automatic rollback4. Tokenization co-evolution through boundary stress detection and vocabulary expansion Includes complete implementation code, experimental results, and reproduction guide. Keywords: language models, recursive self-improvement, inference-time control, tokenization, self-optimization, AI safety, behavioral control