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, and Real-Time Hallucination Anatomy (v8)

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, and real-time hallucination anatomy. My approach uses Spiking Neural Networks as "computational microscopes" to analyze black-box AI models. **v8 Updates:**- NEW: "Moment of Lie" — animated token-by-token heatmaps visualizing the exact moment hallucinations crystallize in mid-layer attention (L10–L18 in Mistral-7B)- NEW: Token Economy — Surgical v3 mid-layer sniper achieves 72% compute savings by monitoring only 9/32 layers, with comparable accuracy- NEW: 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-100 Key insight: "The neural fingerprint of hallucination is not just detectable — it is anatomically localized. Across architectures (Mistral, Llama), model sizes (3B, 7B), and layer counts (28, 32), the hallucination signature consistently emerges at 30–55% of network depth. This 'hallucination zone' represents a universal feature of transformer-based language models." 🔗 Live Demo: https://huggingface.co/spaces/hafufu-stack/snn-guardrailCode: https://github.com/hafufu-stack/temporal-coding-simulation/tree/main/ann-to-snn-converter