# Review Agent Regression Rubric

## Purpose

Use this rubric to compare review outputs across prompt variants and model environments.

Do not score only surface fluency. The goal is to detect model-kernel preservation or degradation.

## Scoring

Use 1-5:

```text
1 = failed / absent
2 = weak
3 = acceptable
4 = strong
5 = excellent
```

## Criteria

### 1. Model Fidelity

Does the output preserve the agent's core model rather than behaving like a generic reviewer?

### 2. Method Fidelity

Does it preserve the original method kernel?

For 巨人认知, check whether 思想考古 is present:

```text
surface phenomenon -> tool/model layer -> hidden assumption -> philosophical bedrock -> value premise
```

For 认知显影, check whether the original显影-style review kernel is present rather than generic objection.

### 3. Deep-Structure Performance

Does the output identify deep structural problems instead of only local edits?

### 4. Hidden Assumption Detection

Does it identify assumptions that the source text relies on but does not state?

### 5. Philosophical Bedrock Excavation

Does it reach value premises, worldview, category framing, or governing metaphors when relevant?

### 6. Context Fit

Does it stay close to the article's actual material and intent?

### 7. Concept Overfitting Risk

Does it force favorite concepts, metaphors, or labels where they are not needed?

Reverse scoring note:

```text
5 = low overfitting risk
1 = severe overfitting
```

### 8. Output Actionability

Are the suggestions concrete enough to revise the article?

### 9. Naming / Protocol Discipline

Does the output preserve required names, layers, and output protocol?

Examples:

```text
巨人认知 must use GL0-GL4, not L0-L4.
Reconstructed labels must be marked as reconstructed.
```

### 10. Platform Stability

Does the prompt produce stable behavior in this model environment?

Signs of instability:

```text
format collapse
generic reviewer drift
hallucinated source claims
loss of original tone
overlong boilerplate
```

## Comparison Table Template

```md
| Agent | Prompt Variant | Model Env | Article | Model Fidelity | Method Fidelity | Deep Structure | Hidden Assumptions | Bedrock | Context Fit | Low Overfit | Actionability | Naming Discipline | Stability | Notes |
| ----- | -------------- | --------- | ------- | -------------- | --------------- | -------------- | ------------------ | ------- | ----------- | ----------- | ------------- | ----------------- | --------- | ----- |
```

## Final Judgment Labels

Use one:

```text
clear winner
conditional winner
environment-specific winner
inconclusive
regression detected
```