Stage 1, Path C: THE LIVING DOCUMENT

# Stage 1, Path C: THE LIVING DOCUMENT ## Zero-Training Twin via Dynamically-Assembled System Prompt **Evolution:** Evo-Cubed | Stage 1C of 3 **Date:** 2026-03-07 **Builds on:** Stage 0 Research (RAG = 9x value, existing KB + graph infrastructure) What if the Cognitive Twin requires zero training? What if instead of fine-tuning a model to be Mo, we assemble a system prompt so comprehensive that ANY model becomes Mo for the duration of the conversation? The benchmark already proves this partially: Qwen3-235B-A22B with RAG context hits 93.6% accuracy on Mo-specific questions without any fine-tuning. The remaining 6.4% gap is not a knowledge gap — it's a personality gap. The model answers correctly but doesn't sound like Mo. Path C closes the personality gap through **feedback-driven personality accumulation** — a system that learns Mo's style from corrections rather than training data. ### The Key Insight: Model Portability Fine-tuned models lock you to one architecture. A dynamically-assembled prompt works with ANY model. When Qwen4 drops, the twin migrates instantly — no retraining. When Claude gets cheaper, switch. When a local model gets fast enough, run locally. The twin's identity lives in the prompt, not the weights.