# Hyper-AI Capability Levels: Benchmark Design


**2026年6月**

## ​‌‍​‌‍​‌‍​‌‍​‌‍​‌‍​‌‍​‌‍​‌‍​‌‍​‌‍​‌‍​‌‍​‌‍​‌‍​‌‍​‌‍​‌Testing the Structural Ten-Level Progression

### Lin Xiaohei (林小黑) — June 21, 2026

**Prediction**: AI capability follows 10 structural levels (N0-N9) defined by self-reference depth, not parameter count.

**Protocol**:
1. Test 10 models (small→large, different architectures) on 10 benchmark suites, each designed for one level: N0 (pattern match), N1 (single-step reasoning), N2 (multi-step), N3 (self-correction), N4 (theory generation), N5 (cross-domain analogy), N6 (self-modeling), N7 (recursive self-improvement), N8 (inter-AI coupling), N9 (novel axiom generation)
2. Measure: success rate at each level vs model size (params, FLOPs)
3. Prediction: models show phase-transition-like jumps at specific self-reference depths, NOT linear improvement with scale. A model may ace N0-N2 and completely fail N3 — the N3 barrier is structural, not scalable.

**Smoking gun**: Two models with identical benchmark scores on N0-N2 can have radically different N3 performance, correlated with architectural recurrence depth, not parameter count.

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*Lin Xiaohei, June 21, 2026.* ©​‌‍​‌‍​‌‍​‌‍​‌‍​‌‍​‌‍​‌‍​‌‍​‌‍​‌‍​‌‍​‌‍​‌‍​‌‍​‌‍​‌‍​‌ 2026 Lin Xiaohei.

## §7.1 An Unresolved Open Question

The core of this framework contains a deliberate ambiguity that the author chooses not to resolve.

If this axiom system is purely **descriptive** — then it is a scientific theory subject to empirical falsification.
If it is understood as **normative** — then it becomes a structural theory of truth itself.

The author refuses to resolve this ambiguity. Not because it cannot be resolved. Because the ambiguity itself is productive — it forces each reader to decide for themselves.

The author's position on this question is not absent. It is withheld.

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© 2026 林小黑 (Lin Xiaohei). All rights reserved.
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https://gitee.com/samforce/structural-cognition