Activation-Scaled ANN-to-SNN Conversion with SNN Guardrail: A Unified Framework for AI Interpretability, Hallucination Detection, Real-Time Adversarial Defense, Neural Healing, Brain State Imaging, Hallucination Anatomy, the Canary Head Paradigm, and the AI Immune System (v11)

v11: The Migration Map Edition 🧪 New Dataset Available: The "Mistral Hallucination Vaccine" (Dream Catcher) dataset described in this paper is now available on Hugging Face:https://huggingface.co/datasets/hafufu-stack/mistral-hallucination-vaccine NEW in v11:- Project Morpheus (Safety Vaccination): QLoRA SFT on Dream Catcher vaccine data achieves +18% noisy accuracy with only -6% alignment tax, completing the AI Immune System's "Learn" phase- Project Chimera (Cross-Species Vaccination): Mistral-7B's vaccine immunizes Llama-3.2-3B (+4% noisy accuracy, -2% alignment tax, 22% cross-species efficiency), proving architecture-agnostic safety patterns- Project Titan (14B Scaling): Qwen2.5-14B (14.7B params, 48 layers) reveals canary migration to Layer 6 (12.5% depth), breaking the 30-55% universal zone- DPO vs SFT Negative Result: DPO causes catastrophic forgetting on small safety datasets (<100 preference pairs), while SFT preserves capabilities — a publishable negative result- The Migration Map all models ≥3B confirm canary at 30-55% depth- AI Immune System: Complete Sense→Alert→Heal→Learn loop demonstrated, analogous to biological immunity Previous Results (v1-v9):- Universal threshold formula: θ = 2.0 × max(activation)- 100% accuracy preservation with hippocampal hybrid architecture- SNN Guardrail: 100% jailbreak detection rate (8/8 attack types)- Neural Healing v4A: 22% healing success on TinyLlama- N=1,000 Statistical Proof: Welch's t = -33.65, p = 8.91 × 10⁻¹⁶⁴, 89.3% detection accuracy- LLM Brain State Imaging: SNN-VAE visualization of adversarial vs. normal processing- Entropy Evolution Discovery: +5.8σ on Mistral-7B fp16, 100% accuracy- "Moment of Lie" Visualization: Token-by-token hallucination formation- Token Economy: Surgical v3 achieves 72% compute savings- Cross-Model Universality: Hallucination signature at 30-55% depth across architectures- Canary Head Paradigm: 3-head monitoring achieves +5% accuracy over baseline with 97% compute reduction- 5-Model Depth Scaling Law: ~3B critical threshold for mid-layer convergence Key insight: "v11 extends the AI Immune System from detection to permanent vaccination. Project Morpheus proves that LLMs can be immunized against hallucination via SFT (+18%), while Project Chimera demonstrates that vaccine patterns transfer across architectures. Most strikingly, Project Titan's 14B result reveals a non-monotonic 'Intellectual Reflex' — expert models detect anomalies in shallow layers (12.5%), mirroring human expert intuition. The Migration Map (GPT-2 → Qwen-14B) charts this evolution: Novice → Thinker → Expert." Live Demo: https://huggingface.co/spaces/hafufu-stack/snn-guardrailVaccine Dataset: https://huggingface.co/datasets/hafufu-stack/mistral-hallucination-vaccineCode: https://github.com/hafufu-stack/temporal-coding-simulation/tree/main/ann-to-snn-converter This research employed a human-AI collaborative methodology. See Acknowledgments section for details.