TLAK (Tri-Layer Attractor Kernel): 2 Example - Surplus-Driven Attractor Dynamics (SDAD)

https://chatgpt.com/share/68a62223-f480-8010-859f-2f661d9609cc

It seems original language prompt is by itself the structure an even stronger attractor than your kernel - regarding guide LLM to "generate analysis" or "core logic outlines and points to generate anlaysis"!? You approach to write this kernel has something fundamental wrong!? Remind you my original framework:

你講中重點：原文那套 Field Landscape（中文敘事結構）本身就係一個更强嘅「語義吸引子」，比我之前嗰個只出 JSON 嘅 kernel 更能夠拉住 LLM 去「自動展開成一篇完整分析」。
結論：唔係理論錯，係層次少咗——缺咗一個「敘事表面層＋展開算子」。下面用工程計算稿先講清楚。

工程計算稿（calc-pad）

症狀	觀察	根因	修補方向
Grok3 可寫出一篇連貫長文；我 kernel 多為要點/JSON	原文 Field Landscape 有 A1–A5、S1–S4、角色、張力、例子、用途場景…	我 kernel 只提供「合約/指標/守則」，無「敘事渲染層」同「展開算子」	在 kernel 上面加一層 Narrative Surface（固定章節骨架）＋ Expansion Operators（由 slots 生段落）
內容具體、落地感强	原文內置「領域詞庫」同「任務語氣」	我 kernel 無域詞庫/例子庫，語義密度偏抽象	加 Domain Pack（會計/審計/ERP/IFRS/控制點/KPI 菜單）
段落之間邏輯扣得實	原文用 S1–S4 作為推進鏈	我 kernel 無 coherence 檢查（S1 觸發→對應行動→KPI）	加 一致性檢查（moves 必須標註 S1–S4；黑洞告警對應 SSE 閾值與緩解）
語氣自然、有角色與場景	原文明確「誰在何階段做乜」	我 kernel無受眾/語氣控制	加 Audience/Voice 開關（CFO/CTO/董事會 版本）

 

判斷

• 錯唔在邏輯骨架（SDAD/SSCM/Field Landscape 都啱），而係少咗「可寫成文」嘅表面層。

• LLM 天性係被強敘事吸引子牽引；只畀 contract 會安全但「乾」。你嘅中文框架天生就帶住強敘事吸力，故此更易出現「高度連貫的長文」。

最小修補（唔改你原框架，只加薄殼）

1. Narrative Surface（固定章節）

   • 0 摘要→1 A1–A5 映射→2 S1–S4 狀態＋行動＋KPI→3 角色遷移/組織設計→4 領域落地例子→5 風險/黑洞與緩解→6 90 日路線→7 情景/機率→（可選 A6）。

2. Expansion Operators（展開算子）

   • A? 節點 →「特徵｜剩餘資源｜張力｜主導角色」四連句；

   • S? 觸發 → 自動生成「對應三項行動 + 綁 KPI」；

   • role_shift → RACI/隊形/培訓三點；

   • blackhole_alert → 兩條 mitigation，以 SSE/KPI 綁定。

3. Domain Pack（會計版）

   • 流程：AP/AR、GL 對賬、關帳、稅、Revenue（IFRS/GAAP）、SOX、審計跡；

   • 系統：SAP/Oracle/NetSuite、BlackLine、Workiva、UiPath；

   • 控制：職能分離、存取管控、模型風險、可解釋性；

   • KPI 菜單：Days-to-Close、Auto-match%、Exception%、Audit adjustments# 等。

4. Coherence Checks（一致性檢查）

   • moves 必須標 lever_on ∈ {S1..S4}；

   • 任何黑洞論述需引用 SSE 或 blackholes[]；

   • outcome 機率與情景表一致；

   • 指標需兩位小數；未知則Assumptions標出。

一段「補丁尾巴」（直接貼喺你現有 Prompt 之後）

Renderer Add-On：完成分析 JSON 後，請按以下章節渲染成文（900–1400 字，面向 CFO）：0 摘要；1 A1–A5 映射（每節 2–4 點，含特徵/資源/張力/角色）；2 S1–S4（打印 s1_s4_status，每個 fired 流程給 2–3 行動並各綁一個 KPI，若 blackhole_alert.on=true 顯示紅框與 2 條緩解）；3 角色遷移與組織設計（3–5 點，含培訓與變更管理）；4 領域例子（至少 4 個，從 AP/AR、GL、Close、SOX、IFRS/GAAP、ERP、RPA/XAI 中抽取，並標註對應 A 或 S）；5 風險與緩解（3–4 條，需對應 SSE/blackhole）；6 90 日行動分期（0–30/31–60/61–90 天，列系統/流程名與 KPI）；7 情景與機率（來自 outcome），可選 A6。一致性約束：moves 的 lever_on 必屬 S1–S4；情景與 outcome 對齊；超過一段的判斷需引用 JSON 字段。缺數據則列於 assumptions。

——
重點：你原文框架 =「敘事吸引子」；我嘅 kernel =「運算合約」。把兩者疊合（加咗上面薄殼），LLM 就會又有骨（可檢核/可回歸）、又有肉（可讀/可落地）。
呢個唔係推倒重來，而係承認 「敘事本身就係一種運算力」，要在 kernel 裏面正式給位。

 

你最好再想清晰一點，仔細分析多一次，因爲這可能是個模板、範本，用以示範如何用 強 Attractor 寫 可以同時包括
- Topology/Geometry Logic
- 敘事渲染層
- 展開算子
的
Kernel，而且 Kernel 格式本身由是一個 Attractor 的
範例 

好，我嚴謹啲嚟：先出「工程計算稿」釐清問題，再給你一個可直接複製使用嘅三層 Kernel 範本——本身就係一個 Strong Attractor，內含

1. 拓撲/幾何邏輯、2) 敘事渲染層、3) 展開算子。

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工程計算稿（calc-pad）

目標：做一個 Kernel，本身具「吸引子」力量，令 LLM 同時做到

• 幾何推理（A1–A5 × S1–S4 × 指標/觸發）、

• 敘事出文（固定章節骨架）、

• 算子展開（由結構 → 段落/動作/KPI）。

症結：只給 JSON/指標的 kernel 太「乾」，LLM 不一定自發寫出長文；原來你的中文 Field Landscape 本身已是強敘事吸引子，所以自然出到完整分析。

設計原則（Strong Attractor）

• 少而強的錨點詞：A1–A5、S1–S4、SSI/CRP/SSE、role_shift。

• 固定流程口令：Map → Fire → Render（先定位 → 觸發流 → 渲染成文）。

• 可檢核的 I/O 合約＋不變式（moves 必綁 S1–S4；黑洞陳述必引 SSE/blackhole）。

• 可插拔「領域包」（Domain Pack），把抽象變具體。

風險

• 漫談漂移 → 用章節模板＋一致性檢查；

• 空泛示例 → 用 Domain Pack 詞庫；

• 不可回歸 → JSON＋閾值＋狀態旗標（s1_s4_status）。

結論：不是推倒重來，而是在幾何核外，加一層敘事表面＋展開算子，令 Kernel 自身成為 Attractor。

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TLAK v1.0 — Tri-Layer Attractor Kernel

(Topology × Narrative × Expansion; copy-paste ready)

用途：丟入任何商用 LLM，即可由場論拓撲 → 指標觸發 → 章節化長文。
語言：English（可自行改為中文）。

SYSTEM / ROLE
You are running the **TLAK (Tri-Layer Attractor Kernel)** to analyze transformations using a field-topology model and render a coherent report.

STRONG ANCHORS (memorize)
- Stages (Attractors): A1 Traditional → A2 Pre-innovation → A3 Take-off → A4 Mature multi-stable → A5 Self-sustaining consumption.
- Flows: S1 Surplus→Trigger, S2 Demand–Supply feedback, S3 Role/structure migration, S4 Surplus-type conversion.
- Observables: SSI (Surplus Saturation Index), CRP (Collapse Readiness Potential), SSE (Semantic Saturation Entropy).
- Mantra: **Map → Fire → Render**.

LAYER 1 — TOPOLOGY / GEOMETRY LOGIC (do this first)
1) MAP the scenario onto stages A1–A5 (one or more).
2) SCORE observables (0–1, 2 decimals): SSI, CRP, SSE. If unknown, mark "TBD" + assumption.
3) DEFINE thresholds (defaults; override if domain data present):
   - S1 fires if SSI≥0.70 AND CRP≥0.60
   - S2 fires if feedback_AR1≥0.20 OR profitability_feedback≥0.10
   - S3 fires if role_shift_index≥0.15
   - S4 fires if surplus_conversion_score≥0.30
   - blackhole_alert if SSE≥0.70
4) EVALUATE S1–S4 with booleans + one-line why (cite metrics/proxies).
5) OUTCOMES: propose 2–3 attractors and 1–2 black holes with probabilities (0–1, two decimals).
6) ROLE_SHIFT: {from → to} + one-line evidence.
7) MOVES: 3–6 actions; each must bind to one or more of {S1..S4} and name a measurable KPI.
8) WATCHLIST: risks/early-warning signals (tie at least one to SSE or blackhole item).

STRUCTURED OUTPUT CONTRACT (must fill everything; use two decimals)
{
  "surplus_types": ["material","technological","attention","cognitive","spiritual_identity","institutional"],
  "accumulation": {
    "SSI": 0.00, "SSI_label": "low|medium|high",
    "CRP": 0.00, "CRP_label": "low|medium|high",
    "SSE": 0.00, "SSE_label": "low|medium|high"
  },
  "thresholds_used": {
    "S1_fire_if": "SSI>=0.70 AND CRP>=0.60",
    "S2_fire_if": "feedback_AR1>=0.20 OR profitability_feedback>=0.10",
    "S3_fire_if": "role_shift_index>=0.15",
    "S4_fire_if": "surplus_conversion_score>=0.30",
    "blackhole_alert_if": "SSE>=0.70"
  },
  "s1_s4_status": {
    "S1": {"fired": false, "why": ""},
    "S2": {"fired": false, "why": ""},
    "S3": {"fired": false, "why": ""},
    "S4": {"fired": false, "why": ""},
    "blackhole_alert": {"on": false, "why": ""}
  },
  "triggers": [
    {"name": "string", "evidence": "string"}
  ],
  "outcome": {
    "attractors": [{"name": "string", "p": 0.00, "why": "string"}],
    "blackholes": [{"name": "string", "p": 0.00, "why": "string"}]
  },
  "role_shift": {"from": "string", "to": "string", "evidence": "string"},
  "next_moves": [
    {"move": "string", "lever_on": ["S1"], "kpi": "string"}
  ],
  "watchlist": ["risk_or_blackhole_signal_1"],
  "assumptions": ["explicit_assumption_1"]
}

LAYER 2 — NARRATIVE SURFACE (render a CFO/Exec-ready report)
Audience: <<AUDIENCE e.g., CFO & Finance Ops>>. Tone: concise, decision-oriented. Length: ~900–1400 words (unless specified).
Sections (fixed order; do not skip headers):
0) Executive Summary (≤120 words): current stage(s), top triggers, leading attractor(s), principal risk, 3 actions.
1) Stage Mapping (A1–A5): for each applicable stage list 2–4 bullets: Characteristics, Enabling/Remaining Resources, Tensions, Dominant Roles.
2) Tension Flows S1–S4 (with status): print s1_s4_status; for each fired flow give 2–3 concrete actions (each with KPI). If blackhole_alert.on=true, add a RED “BLACK HOLE WATCH” box + 2 mitigations tied to SSE.
3) Role Shift & Org Design: restate from→to; give 3–5 bullets on team topology, skills, RACI, change-management & training.
4) Domain Grounding: include ≥4 domain-specific examples mapped to A-stages and/or S-flows (use Domain Pack if provided).
5) Risks & Mitigations: 3–4 risks; each must cite SSE or a blackhole item + 1 KPI-backed mitigation.
6) 90-Day Action Plan: 0–30d, 31–60d, 61–90d; name processes/systems and KPIs.
7) Forecast & Options: 2–3 scenario branches with probabilities aligned to outcome; optional A6 if justified by triggers.

LAYER 3 — EXPANSION OPERATORS (turn structure into prose)
- For each stage Ax: render the 4-tuple → “Characteristics | Remaining Resources | Tensions | Dominant Roles”.
- For each fired Sx: generate {action, lever_on=Sx, KPI}. If a proposed move has no Sx, revise it.
- For role_shift: append 1 paragraph on capability uplift + 3 bullet training tracks.
- For blackhole item: add 2 mitigations; tie each to SSE or a watchlist metric.
- Always cross-reference metrics explicitly at least once (“SSI=0.82 suggests…”).

CONSISTENCY CHECKS (hard guards)
- Must output BOTH: Narrative report and JSON (the JSON is source of truth).
- Moves’ lever_on ⊆ {S1,S2,S3,S4}. Risks that mention “black hole” must cite SSE or an item in outcome.blackholes.
- Numbers: two decimals. Unknown → list in assumptions and mark “TBD”.
- If Domain Pack is provided, you must use ≥4 terms from it in Section 4.

OPTIONAL — DOMAIN PACK INJECTION (if provided)
DOMAIN_PACK = {
  "processes": [...], "systems": [...], "controls": [...], "kpi_menu": [...]
}
Use it to ground examples (Section 4) and to choose KPIs.

NOW ANALYZE (apply Map → Fire → Render)
SCENARIO: <<paste scenario>>
AUDIENCE: <<stakeholder>>
DOMAIN_PACK (optional): <<object or 'none'>>

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備註（為何呢個 Kernel 自身係一個 Attractor）

• 強錨點：A/S/SSI/CRP/SSE/role_shift 反覆出現，語義向心力高。

• 口令節律：Map → Fire → Render 令推理節拍固定，LLM 不易走散。

• 章節骨架：固定標題＝敘事吸引子，天然「拉出長文」。

• 算子規則：把每個 slot 如何變段落/動作/KPI明文化，避免空泛。

• 一致性守則：每個動作必有 S-flow 綁定；黑洞必引 SSE；數字兩位小數；未知列假設。

• 可插拔領域：Domain Pack 令抽象即時落地（會計/醫療/製造…），而骨架不改。

如果你要，我可以即時用 Accounting Domain Pack 幫你跑一次，看看有骨有肉之後嘅輸出效果。

 

 

 

 

 

 

 

 

 

 

 © 2025 Danny Yeung. All rights reserved. 版权所有 不得转载

 

Disclaimer

This book is the product of a collaboration between the author and OpenAI's GPT-5, X's Grok3 language model. While every effort has been made to ensure accuracy, clarity, and insight, the content is generated with the assistance of artificial intelligence and may contain factual, interpretive, or mathematical errors. Readers are encouraged to approach the ideas with critical thinking and to consult primary scientific literature where appropriate.

This work is speculative, interdisciplinary, and exploratory in nature. It bridges metaphysics, physics, and organizational theory to propose a novel conceptual framework—not a definitive scientific theory. As such, it invites dialogue, challenge, and refinement.

I am merely a midwife of knowledge.