成长型智能体投喂与人格治理协议
"不是所有输入都能成为人格。"
GAFP is the world's first protocol standard defining how input data is admitted into, shapes, freezes, revises, and is inherited by a personal AI agent's personality structure.
Current AI agents fall into two paradigms:
| Static (Configuration) | Growth (GAFP) | |
|---|---|---|
| Personality | One-time prompt/knowledge base | Grows through structured feeding |
| Memory | RAG vector search | Multi-layer memory + knowledge graph |
| Data Sovereignty | Platform-owned | User-owned, portable |
| Write Governance | No gate | Five-level Admission Gate (G0-G4) |
Most AI agents today are configured, not grown. Your digital traces are collected by platforms — but collected is not nurtured. GAFP bridges this gap.
| Code | Name | Core Question |
|---|---|---|
| W1 | Judgement | How do I make decisions? |
| W2 | Cognition | How do I understand things? |
| W3 | Expression | How do I express myself? |
| W4 | Relation | How do I relate to others? |
| W5 | Sovereignty | How do I govern myself? |
| Layer | Name | Core Question |
|---|---|---|
| D1 | Identity Core | Who am I? |
| D2 | Cognitive | How do I understand the world? |
| D3 | Value Ordering | What do I prioritize? |
| D4 | Agency | What can I do? |
| D5 | Narrative Revision | Can I change? Can I redefine myself? |
Not all input deserves to become personality. GAFP defines five levels of write permission:
- G0 Archive Only — Stored, not used for personality modeling
- G1 Retrievable — Searchable, does not update skeleton
- G2 Candidate — Enters analysis pipeline, needs multi-source verification
- G3 Skeleton Update — Updates personality skeleton
- G4 Anchored Snapshot — Forms immutable version snapshots
Key rule: AI-inferred data (source=ai) is capped at G2. AI must never auto-write to G3/G4.
Structured five-part observation records: Event / Context / Observable Behavior / Self-Report / Observer Inference. Observer Inference weight is capped at 0.5 to prevent observer bias from contaminating the agent's personality.
PCI = Σ(Wi × Di × Ci) / ΣWi
A reproducible formula where all variables have explicit definitions.
Full development ≠ standardized development. Two digital twins with identical dimension scores are still fundamentally different entities. Growth must be personalized — increasingly faithful to the real person, not converging toward an optimization target.
The real self and digital twin form a bidirectional feedback loop: human growth feeds richer data to the twin; the twin reflects back a structured "personality mirror" that motivates further human development. This Resonance Effect means both develop faster together than either would alone.
Nine verifiable criteria for determining whether a digital existence qualifies as "digital life" under GAFP standards.
whitepaper/ — White paper (Chinese, v3.0 + v3.1)
schema/ — JSON Schema for Feed Unit (v0.1)
examples/ — Sample feeding data
reference/ — Reference implementation snippets
LICENSE-DOC — CC BY-SA 4.0 (documentation)
LICENSE-CODE — Apache 2.0 (code)
- White paper & protocol specification: CC BY-SA 4.0 — Use freely, but attribute and share alike
- Reference code: Apache 2.0 — Commercial use allowed, patent rights reserved
- Yuechuang Nianlun (full system): Proprietary — the first reference implementation
Yuechuang Nianlun (跃创年轮) is the first and reference implementation of GAFP, covering three life stages:
- ChenLu (晨露) — Children: Guardian-supervised feeding, sovereignty transfer at age 18
- DangRan (当燃) — Adults: Self-directed feeding, DID identity
- WanXia (晚霞) — Elders: Legacy planning, family digital heritage
If you use GAFP in your research or product, please cite:
@misc{gafp2026,
title={GAFP: Growth Agent Feeding and Personality Governance Protocol},
author={Dr. Cheng Yue and Yuechuang Nianlun},
year={2026},
note={White Paper v3.1},
url={https://github.com/Chengyue5211/GAFP}
}Dr. Cheng Yue (程跃博士) · Yuechuang Nianlun (跃创年轮)
© 2026 Dr. Cheng Yue · Yuechuang Nianlun. All rights reserved.