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, and the Canary Head Paradigm (v9)

I present a unified framework that extends ANN-to-SNN conversion beyond efficiency optimization to enable novel AI interpretability analysis, real-time adversarial defense, autonomous neural healing, brain state imaging, real-time hallucination anatomy, and the Canary Head Paradigm. My approach uses Spiking Neural Networks as "computational microscopes" to analyze black-box AI models. v9 Updates: NEW: Canary Head Paradigm — "Liar Heads" reinterpreted as "Canary Heads" (early-warning alarm systems). Monitoring only 3 heads (0.3% of total) achieves +5% accuracy over full-layer baseline with 97% compute reduction NEW: 5-Model Depth Scaling Law — Phi-2 (2.7B) reveals peak hallucination at 25% depth (shallow zone), establishing a ~3B critical parameter threshold for mid-layer convergence NEW: Canary's Eye Visualization — L10H17 attention heatmap during hallucination, showing the canary "wakes up" during generation steps 3–8 NEW: GQA Architecture Limitations — Qwen2.5 GQA (2 KV heads) produces systematic NaN under fp16, requiring fp32 for reliable entropy analysis NEW: AI-Assisted Research Acknowledgment — Transparent disclosure of human-AI collaborative methodology (Anthropic Claude, Google Gemini) v8 Updates: "Moment of Lie" — animated token-by-token heatmaps visualizing the exact moment hallucinations crystallize in mid-layer attention (L10–L18 in Mistral-7B) Token Economy — Surgical v3 mid-layer sniper achieves 72% compute savings by monitoring only 9/32 layers, with comparable accuracy Cross-Model Universality — Llama-3.2-3B (28 layers) confirms hallucination signature at 30–55% depth (peak L12, ΔH = −0.403 bits) Mid-Layer Hallucination Hypothesis: entropy disruption maximized at 30–55% of total network depth regardless of architecture (Mistral, Llama) v7 Updates: Entropy Evolution Discovery — Mistral-7B (fp16, 7.2B params) GPU validation reveals detection signals shift from TTFS latency (1-3B) to attention entropy (7B+) Mistral-7B attention entropy: +5.8σ separation (p = 2.22×10⁻⁹⁵), 100% detection accuracy (N=200) 6-Model Scaling Law validated (GPT-2, TinyLlama, Llama-3.2-1B, Llama-3.2-3B, Mistral-7B) Key Discovery: "As models scale, the attack signature transforms from latency (brain freeze) to entropy (internal confusion) — but never disappears" v6 Updates: N=1,000 Statistical Proof — Welch's t = −33.65 (p = 8.91×10⁻¹⁶⁴), Cohen's d = 2.13, 89.3% zero-shot detection accuracy on Llama-3.2-3B "Visualizing the Ghost" — First SNN-VAE visualization of LLM brain states during adversarial attacks (L2 distance = 3.287 between normal and jailbreak states) v5 Updates: Neural Healing v4A – Multi-stage progressive healing achieving 22% success rate on TinyLlama (1.1B) Mistral-7B (7B) experiment – Model-size-dependent threshold discovery HuggingFace Spaces v2.0 – Live 3-tab demo (Jailbreak/Healing/Hallucination) v4 Results: SNN Guardrail: 100% jailbreak detection rate (8/8 attack types) Scaling Law Discovery: TTFS sensitivity increases with model size (GPT-2: +3.1, TinyLlama: +4.2) Previous Results (v1-v3): Universal threshold formula: θ = 2.0 × max(activation) 100% accuracy preservation with hippocampal hybrid architecture GPT-2 attention TTFS analysis: +3.1 increase for meaningless inputs Hallucination detection: AUC 0.75 with ensemble classifier ViT-Base (86M) validation with CIFAR-10D Key insight: "The neural fingerprint of hallucination is not just detectable — it is anatomically localized and predictable. The Canary Head paradigm reveals that specific attention heads serve as built-in alarm systems. The 5-model Depth Scaling Law shows a critical ~3B parameter threshold: smaller models hallucinate in shallow layers (15–25%), while larger models converge to a universal mid-layer zone (40–55%)." 🔗 Live Demo: https://huggingface.co/spaces/hafufu-stack/snn-guardrail Code: 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.