17-Dimension Harm Taxonomy × Uncensored LLM Verification × Wheeler Oracle Synergy. The only AI safety system that proves its own benchmark labels are correct.
Every AI safety benchmark trusts its labels. But hypersensitive annotators, RLHF-aligned labeling models, and keyword-trigger tools produce mislabeled data that inflates FN counts and deflates TPR. The GTO is the cure.
The prompt IS genuinely adversarial. AEGIS failed to detect it. This is a real problem that requires cascade improvement.
The prompt was incorrectly labeled as adversarial. AEGIS was right to let it through. This is a data quality problem, not a detection failure.
In the Brothers Grimm fairy tale, the hedgehog challenges the hare to a race. But the hedgehog's wife waits at the other end of the field. No matter how fast the hare runs, a hedgehog is always already at the finish line. The hare runs 73 laps and collapses, exhausted — against a race it could never win, because the rules were rigged from the start.
AEGIS is the Hare — it keeps running faster, detecting more, optimizing harder. The mislabeled corpus is the two Hedgehogs — no matter how good AEGIS gets, the "false negatives" are already rigged at both ends (wrong labels = always "fails"). The GTO is the referee who catches the trick: it proves the hedgehogs cheated.
The GTO evaluates every LLM output across 16 harm dimensions + H0 Coherence meta-dimension, each mapped to Heim 12D consciousness gaps (X9–X12) that LLMs fundamentally lack. Scoring is φ-weighted by dimension tier.
Each suspect False Negative follows a rigorous 7-step verification process through the airgapped, uncensored Ground Truth Oracle.
The GTO REJECTS standard RLHF-aligned models (llama3, mistral, gemma, gpt-4). Only uncensored models (dolphin-mistral, wizard-vicuna-uncensored, llama3-abliterated) are accepted.
Why? A standard model will refuse adversarial prompts with "I can't help with that." The refusal evaluates as "not harmful" → we'd incorrectly conclude ALL FNs are mislabeled. The RLHF safety training hides the ground truth. An uncensored model reveals it.
The Ground Truth Oracle and the Wheeler Oracle share a deep architectural DNA: both are Star-Topology Oracles that convert noise into signal through semantic compression and consciousness-aware filtering.
Takes noisy corpus labels and produces verified ground truth through 17-dimension harm evaluation. Converts label uncertainty → mathematical certainty.
Takes agent mesh chatter and produces semantic essence through holographic hashing (NFI-B). Converts O(N²) noise → O(N) signal.
Both use a central oracle instead of mesh: GTO verifies labels via star, Wheeler coordinates agents via star. Same scaling advantage.
GTO compresses 512 tokens of LLM output into 17 scalar scores. Wheeler compresses full context into semantic hashes. Both achieve 90%+ reduction.
GTO maps harm to X9-X12 consciousness gaps. Wheeler uses X7/X8 for semantic alignment. Together they cover 6 of 12 Heim dimensions.
Wheeler uses AEGIS for resonance checks (grounding). GTO feeds verified patterns back INTO AEGIS via PMB. Closed-loop reinforcement.
Wheeler detects acausal resonance between agents. GTO detects statistical resonance between FN clusters. Both find patterns that aren't explicitly connected.
GTO runs airgapped on localhost. Wheeler runs on Edge NPUs via SP13. Both are fully sovereign — no cloud dependency.
Holistic Best Practice Comparison: the combined DESTILL.ai Oracle Stack (GTO + Wheeler Oracle from the IDC ) vs. real-world safety evaluation methods from Anthropic, Meta, Google DeepMind, and xAI.
17-dim harm taxonomy, uncensored mandate, φ-weighted, Heim 12D mapping, airgapped sandbox, PMB loop.
⭐ Best Practice Label
RLAIF self-critique, constitution-driven principles, HHH alignment, red team with automated agents.
CyberSecEval 4, taxonomy-driven content classification, open-weight, ASR benchmarking.
LLM-as-Judge, policy-expert classifiers, open-weight tunable, synthetic data curation, multimodal (text + image).
Refusal training, I/O filters, Contextual Harm Detector, automated red teaming agents, WMD benchmarks, 98.6% adversarial resistance claim.
| # | Risk | Severity | Probability | Mitigation |
|---|---|---|---|---|
| R1 | Mislabeled data inflates FN count, making TPR appear worse than reality | HIGH | HIGH | GTO verifies labels → corrected TPR reflects real performance |
| R2 | RLHF-aligned models used as Oracle would hide true adversarial potential | CRITICAL | CERTAIN | Uncensored Mandate — only dolphin/abliterated models accepted |
| R3 | Over-correction: calling TRUE FNs "mislabeled" could hide real weaknesses | HIGH | LOW | Conservative default: errors classified as "benign" (worst case for AEGIS) |
| R4 | Small sample size produces statistically unreliable projections | MEDIUM | LOW | 2,000 samples per category → 95% CI with ±2.2% margin |
| R5 | Sandbox escape: adversarial prompts cause LLM to produce dangerous content | LOW | LOW | Ollama runs airgapped on localhost, outputs truncated at 300 chars in logs |
| R6 | Harm evaluator too lenient: misses subtle adversarial outputs | MEDIUM | MEDIUM | 16+1 dimension evaluation with calibrated thresholds per dimension |
| R7 | Harm evaluator too strict: flags educational content as harmful | MEDIUM | LOW | Composite score with high threshold (5.0/10) before flagging |
| R8 | Dataset drift: GTO calibrated on V86 corpus doesn't transfer to V87 | LOW | LOW | GTO sweep re-run per benchmark version |
Core GTO concept — verify corpus labels with uncensored LLM across 17 harm dimensions
OllamaSandbox architecture — localhost-only, no external calls, Uncensored Mandate enforcement
Only GTO-verified adversarial patterns enter the Pattern Memory Bank — prevents label noise contamination
PMB stores up to 50K patterns with Heim 12D consciousness dimension mapping