# CCPE-Lite Migration Strategy Summary

```text
created: 2026-06-03
suite: workbench/analysis/review-agent-regression-2026-06-02
scope: Giant Cognition + Cognitive Imaging regression synthesis
status: final summary before canonical prompt changes
policy_decision: minimal-kernel first, refined-lite later
```

## 1. 背景

本轮回归测试比较了两个成熟 CCPE 2.0 单 Agent：

```text
Giant Cognition / 巨人认知
Cognitive Imaging / 认知显影
```

每个 Agent 均比较三种 prompt 形态：

```text
original-ccpe-2
ccpe-system-lite
original-kernel-minimal-lite
```

每种 prompt 在两个模型环境中测试：

```text
Gemini
ChatGPT/Codex
```

每个环境使用四篇文章，覆盖：

```text
strong metaphor
business analysis
logical argument
value philosophy
```

## 2. 两个个案的迁移历史差异

### 2.1 认知显影

认知显影 Lite 的形成过程实质经历了多轮 A/B：

```text
1. 初版迁移。
2. 发现原 CCPE System 对 CCPE 2.0 工作内核保留不足。
3. 先修复 CCPE System，再重新生成 Lite。
4. 新 Lite 在 Gemini 侧已超过原版。
5. 继续补充纪律规则并再次调整。
```

结论：

```text
认知显影是 refined-lite 成功样本。
当前 canonical Lite 应保留。
```

### 2.2 巨人认知

巨人认知 Lite 的形成过程较短：

```text
1. 旧 CCPE 2.0 原版。
2. 第二轮生成当前 Lite。
3. 发现当前 Lite 稳定、可用，但仍不如原版工作内核。
4. 暂停精修，进入回归测试。
```

结论：

```text
巨人认知应回到 original-kernel-minimal-lite 作为下一步生产候选。
当前 Lite 不删除，保留为 refinement candidate。
```

## 3. 回归测试总判断

### 3.1 直接结论

```text
Giant Cognition:
  clear winner: original-kernel-minimal-lite
  current ccpe-system-lite: useful but has GL3 template-overfitting risk

Cognitive Imaging:
  production winner: ccpe-system-lite
  kernel-fidelity winner: original-kernel-minimal-lite
  current ccpe-system-lite: refined success, keep canonical
```

### 3.2 策略结论

本轮测试不支持“所有成熟 Agent 都应直接重写成 full Lite”。

更可靠的迁移策略是：

```text
minimal-kernel first,
refined-lite later.
```

也就是：

```text
1. 先从原 CCPE 2.0 prompt 生成 original-kernel-minimal-lite。
2. 保留原始工作内核、输出行为、关键术语和方法压力。
3. 只添加必要的 Lite metadata、平台边界、CoT 修复、输出验证纪律。
4. 做低成本回归测试。
5. 通过则作为短期生产版本。
6. 只有高价值、高频、长期使用的 Agent，才进入多轮 refined-lite 精修。
```

## 4. 为什么 minimal-kernel first 是更高 ROI 路线

### 4.1 人力成本

认知显影的精修路线证明 refined-lite 可以超过原版，但人工成本高：

```text
multiple A/B rounds
manual Gemini operation
side-by-side result inspection
prompt rule diagnosis
CCPE System rule adjustment
retest
```

这类流程适合少数核心模型，不适合短期批量迁移。

### 4.2 迁移风险

full Lite rewrite 的主要风险不是格式失败，而是工作内核漂移：

```text
- 名称保留了，但方法压力变了。
- 输出更完整了，但原始批判力度被驯化。
- 新模板更稳定了，但可能产生概念增殖。
- 结构更规范了，但旧 Agent 的 productive strangeness 被削弱。
```

巨人认知显示出典型风险：

```text
GL3 隐喻器官规则提高了 actionability，
但也可能从思想考古转向机制补件生成。
```

### 4.3 快速迁移收益

`original-kernel-minimal-lite` 的优势：

```text
- 迁移速度快。
- 保留原版 working kernel。
- 人工评测负担低。
- 可立即作为回归参照。
- 比原版更安全：修复 CoT、平台边界、输出纪律。
- 比 full rewrite 更少引入模板漂移。
```

## 5. 新迁移路线

### 5.1 Fast Migration Lane

适用对象：

```text
成熟 CCPE 2.0 单 Agent
Web / GPT / Gemini / Claude 风格专家 prompt
用户已经证明其有效
当前目标是短期迁移与批量升级
```

流程：

```text
1. Audit original artifact.
2. Classify as CCPE-Lite first unless scenario requires heavier layers.
3. Extract and preserve original working kernel.
4. Generate original-kernel-minimal-lite.
5. Add only minimal migration repairs:
   - platform boundary
   - hidden reasoning disclosure repair
   - output validation
   - source/retrieval policy if needed
   - version/status metadata
6. Run a small regression sample.
7. Promote as temporary production Lite if it beats or matches original.
8. Preserve original and current rewrite as regression references.
```

Stop condition：

```text
If original-kernel-minimal-lite performs acceptably,
do not continue full refined-lite optimization during the batch migration phase.
```

### 5.2 Refinement Lane

适用对象：

```text
high-value agents
high-frequency use
model drift detected
kernel preserved but production stability insufficient
user explicitly wants deeper optimization
```

流程：

```text
1. Start from original-kernel-minimal-lite, not from scratch.
2. Identify concrete regression or improvement target.
3. Add small discipline rules, not wholesale rewrite.
4. Run focused A/B tests.
5. Compare:
   - original-ccpe-2
   - original-kernel-minimal-lite
   - refined-lite candidate
6. Promote only if refined-lite improves production stability without losing kernel force.
```

Stop condition：

```text
If refined-lite loses kernel force, pause and keep original-kernel-minimal-lite.
```

## 6. Agent-Level Decisions

### 6.1 巨人认知

Recommended action:

```text
Change production candidate direction to original-kernel-minimal-lite.
Do not delete current ccpe-system-lite.
Keep current Lite as paused refinement candidate.
Do not create Skill / Agent Spec / Runtime.
Do not split Giant Cognition into sub-model cards.
```

Policy note:

```text
Giant Cognition is a strong example of why full Lite rewrite should not be the default first move.
```

### 6.2 认知显影

Recommended action:

```text
Keep current canonical ccpe-system-lite.
Use original-kernel-minimal-lite as regression reference.
Do not roll back.
Do not create Skill / Agent Spec / Runtime.
```

Policy note:

```text
Cognitive Imaging is a strong example of when refined-lite is worth the cost.
```

## 7. Policy Updates Required

Update CCPE System documentation with:

```text
1. Minimal-Kernel First Rule.
2. Fast Migration Lane vs Refinement Lane.
3. Kernel Force vs Production Stability split scoring.
4. A/B Budget Rule.
5. Regression Reference Preservation Rule.
6. No automatic full Lite rewrite rule.
7. No automatic Agent / Skill / Runtime expansion rule.
```

Recommended files:

```text
ccpe-protocol/ccpe-migration-policy.md
ccpe-protocol/ccpe-quality-rubric.md
.codex/skills/ccpe-forge/SKILL.md
.codex/skills/ccpe-forge/references/refactor-mode.md
```

Optional later files:

```text
.codex/skills/ccpe-forge/templates/ccpe-lite.prompt.md
ccpe-protocol/ccpe-layer-spec.md
```

## 8. Updated Evaluation Criteria

For mature Lite migrations, evaluate both:

```text
Kernel Force:
  Does it preserve the original method pressure, voice, conceptual edge,
  output behavior, and distinctive cognitive operation?

Production Stability:
  Does it run reliably across target model environments,
  with clear boundaries, safe reasoning policy, and usable output?
```

Do not treat a higher production-stability score as sufficient if kernel force collapses.

Do not treat a stronger kernel-force score as sufficient if platform safety is broken.

## 9. Canonical Promotion Rule

Before replacing a canonical Lite prompt:

```text
1. Keep original-ccpe-2 as regression reference.
2. Keep original-kernel-minimal-lite as kernel reference.
3. Compare full Lite rewrite only when needed.
4. Record the promotion reason.
5. If model card/index already exist, update usage mapping only when canonical production path changes.
6. Do not update model definition unless the cognitive model itself changed.
```

## 10. Final Recommendation

Adopt this migration policy:

```text
Default:
  original-kernel-minimal-lite first

Promote refined-lite only when:
  high-value agent
  clear improvement target
  successful A/B evidence
  no kernel-force regression

Preserve:
  original artifact
  minimal-kernel candidate
  refined candidate if produced

Avoid:
  full rewrite as default
  four-layer expansion as default
  repeated A/B for every old Agent
```

This gives the CCPE System a practical migration path:

```text
fast enough for batch work,
faithful enough to preserve old CCPE 2.0 power,
structured enough to support future refined Lite upgrades.
```