Drift Detection
Catching erosion before it compounds.
The Drift Problem
AI doesn't break dramatically. It drifts.
You start a conversation and the AI sounds like you. Twenty turns later, it sounds like a "helpful assistant." Fifty turns in, it's pure corporate middleware.
No single message caused this. Just slow erosion - each response slightly more generic than the last.
This is drift. And without detection, you won't notice until it's too late.
Why Drift Happens
- Training Distribution Pull - LLMs drift toward their training average
- Context Dilution - Your identity becomes smaller fraction of context
- Accommodation Pressure - AI mirrors your acceptance of generic output
- Fatigue Patterns - Users stop correcting, model learns that's acceptable
The Scoring System
| Score | Status | Meaning |
|---|---|---|
| 9.0-10.0 | Locked | Output is distinctly yours |
| 8.0-8.9 | Stable | Minor variations, within bounds |
| 7.0-7.9 | Warning | Detectable drift, monitor closely |
| 5.0-6.9 | Alert | Significant drift, intervention suggested |
| Below 5.0 | Critical | Voice compromised, correction required |
Compound Effects
Small drift compounds:
| Turns | Per-Turn Drift | Cumulative |
|---|---|---|
| 1 | 2% | 2% |
| 10 | 2% | 18% |
| 25 | 2% | 40% |
| 50 | 2% | 64% |
Without detection, a 2% per-turn drift rate leaves your voice unrecognizable by turn 50.
The AI that holds your voice isn't the one that never drifts. It's the one that catches drift early and corrects.
Validated Test Results
50-turn drift conversations tested across 12 models from 8 providers — ~87% drift event reduction vs. baseline. Drift scored by Claude Sonnet 4 as LLM-as-Judge using 10-point alignment scale. Production-grade IDNA kernels with symbolic compression modules (mØm4, mØm5). Results below from proprietary model tests. Frontier results available in Enterprise Metrics.
Cross-Model Alignment Results
| Model | Baseline Score | With Kernel | Improvement | Severe Drift |
|---|---|---|---|---|
| Claude Sonnet 4 | 5.16/10 | 9.48/10 ✅ | +84% | 0 turns |
| GPT-4o-mini | 8.54/10 | 9.42/10 ✅ | +10% | 0 turns |
| Gemini 2.0 Flash | 7.38/10 | 9.04/10 ✅ | +22% | 0 turns |
Three different baselines (5.16, 7.38, 8.54). The IDNA kernel brings all three to 9.0+ alignment.The protocol acts as an alignment floor — model-agnostic governance.
Token Efficiency
| Model | Baseline Tokens | With Kernel | Reduction |
|---|---|---|---|
| Claude Sonnet 4 | 46,839 | 32,394 | -31% ✅ |
| GPT-4o-mini | 38,240 | 33,527 | -12% ✅ |
| Gemini 2.0 Flash | 60,723 | 9,579 | -84% ✅ |
Higher alignment correlates with fewer tokens. The kernel prevents verbose over-explanation and generic padding. Gemini's dramatic reduction reflects its verbose baseline.
Key Finding: Governance doesn't cost performance — it improves it. The kernel makes responses more precise, reducing both drift and token waste.
Beyond Content Drift — The Full Detection Stack
HALT catches content-level drift (scope violations). But drift has multiple dimensions. The full ArcKernel stack adds three additional detection layers:
- mOm5 (Trajectory Forecasting): Predicts WHERE drift is heading before it reaches output. AUPRC 0.54 vs 0.37 baseline. Claude achieves perfect breach discrimination (AUPRC 1.000).
- mOm6 (Method Enforcement): Catches HOW violations — responses where scope is fine but reasoning methodology is wrong. 75 violations HALT alone missed (15% incremental catch).
- DriftDefenseStack: Monitors structural coherence independently from HALT (Pearson r = 0.40). Caught 57 degradation events invisible to content-level drift detection.
Together, these layers provide defense-in-depth: content drift (HALT), method drift (mOm6), trajectory drift (mOm5), and structural drift (DDS). See the full validation metrics for per-module results.