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 (v10)

v10: The AI Immune System 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 v10:- Lucid Dream Self-Healing: LLMs recover from induced hallucinations with 90% success rate (97.9% at scale with 1,002 samples) via canary-triggered Surgical CoT injection- Canary Genealogy: Instruction tuning changes canary identity (L10H17→L17H13) but preserves the 30-55% depth zone — Hypothesis H1 confirmed, proving the Depth Scaling Law is a structural invariant- Electric Dreams: Hidden-state noise injection reveals canary sensitivity of 1.19× at micro-perturbation (σ=0.01), with phase transition to hallucination at σ=0.10- Dream Catcher Vaccine: Automated synthetic hallucination dataset generation (1,002 clean/nightmare/healed triplets with canary entropy labels) — first fully automated, canary-labeled hallucination dataset for LLMs- 7-Model Depth Scaling Law: Validated across 7 architectures including fine-tuned variants; 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: "The Canary Head Paradigm has evolved into a complete AI Immune System. v10 demonstrates that LLMs can sense perturbation (1.19× canary sensitivity), self-heal from induced hallucinations (97.9% recovery via Surgical CoT), and generate their own training data (Dream Catcher vaccine). The 7-model Depth Scaling Law, confirmed across fine-tuned variants, establishes that the 30-55% hallucination zone is a structural property of transformer architecture." Live Demo: https://huggingface.co/spaces/hafufu-stack/snn-guardrailCode: 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.