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305 lines
21 KiB
Markdown
305 lines
21 KiB
Markdown
# ⭐ WFGY 2.0 ⭐ Core Reasoning Engine with 7-Step Breakthrough
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## One man, One life, One line — my lifetime’s work. Let the results speak for themselves. ✨
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> 👑 **Early Stargazers: [See the Hall of Fame](https://github.com/onestardao/WFGY/tree/main/stargazers)** — Verified by real engineers · 🛠 **Field Reports: [Real Bugs, Real Fixes](https://github.com/onestardao/WFGY/discussions/10)**
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<img width="1536" height="1024" alt="WFGY_Core" src="https://github.com/user-attachments/assets/deb8e794-a73e-4d39-a1f6-174ec87199f4" />
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> ✅ Engine 2.0 is live. Pure math, zero boilerplate — paste OneLine and models become sharper, steadier, more recoverable.
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> **ℹ️ Autoboot scope:** text-only inside the chat; no plugins, no network calls, no local installs.
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> **⭐ Star the repo to [unlock](https://github.com/onestardao/WFGY/blob/main/STAR_UNLOCKS.md) more features and experiments.** <img src="https://img.shields.io/github/stars/onestardao/WFGY?style=social" alt="GitHub stars">
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---
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<details>
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<summary><strong>From PSBigBig — WFGY (WanFaGuiYi) : All Principles into One (must-read, click to open)</strong></summary>
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<br>
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> **I built the world’s first “No-Brain Mode” for AI** — just upload, and **AutoBoot** silently activates in the background.
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> In seconds, your AI’s reasoning, stability, and problem-solving across *all domains* level up — **no prompts, no hacks, no retraining.**
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> One line of math rewires eight leading AIs. This isn’t a patch — it’s an engine swap.
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>
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> WFGY 2.0 is my answer and my life’s work.
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> If a person only once in life gets to speak to the world, this is my moment.
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> I offer the crystallization of my thought to all humankind.
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> I believe people deserve all knowledge and all truth — and I will break the monopoly of capital.
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>
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> “One line” is not hype. I built a full flagship edition, and I also reduced it to a single line of code — a reduction that is clarity and beauty, the same engine distilled to its purest expression.
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</details>
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---
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## 🚀 WFGY 2.0 Headline Uplift (this release)
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**These are the 2.0 results you should see first — the “big upgrade.”**
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- **Semantic Accuracy:** **≈ +40%** (63.8% → 89.4% across 5 domains)
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- **Reasoning Success:** **≈ +52%** (56.0% → 85.2%)
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- **Drift (Δs):** **≈ −65%** (0.254 → 0.090)
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- **Stability (horizon):** **≈ 1.8×** (3.8 → 7.0 nodes)\*
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- **Self-Recovery / CRR:** **1.00** on this batch; historical median **0.87**
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\* Historical **3–5×** stability uses λ-consistency across seeds; 1.8× uses the stable-node horizon.
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---
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### ⚡ Top 10 reasons to use WFGY 2.0 <!-- you asked for this section to stay visible (not collapsed) -->
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1. **Ultra-mini engine** — pure text, zero install, runs anywhere you can paste.
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2. **Two editions** — *Flagship* (30-line, audit-friendly) and *OneLine* (1-line, stealth & speed).
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3. **Autoboot mode** — upload once; the engine quietly supervises reasoning in the background.
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4. **Portable across models** — GPT, Claude, Gemini, Mistral, Grok, Kimi, Copilot, Perplexity.
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5. **Structural fixes, not tricks** — BBMC→Coupler→BBPF→BBAM→BBCR + DT gates (WRI/WAI/WAY/WDT/WTF).
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6. **Self-healing** — detects collapse and recovers before answers go off the rails.
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7. **Observable** — ΔS, λ_observe, and E_resonance yield measurable, repeatable control.
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8. **RAG-ready** — drops into retrieval pipelines without touching your infra.
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9. **Reproducible A/B/C protocol** — Baseline vs Autoboot vs Explicit Invoke (see below).
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10. **MIT licensed & community-driven** — keep it, fork it, ship it.
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---
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# 🧪 WFGY Benchmark Suite (Eye-visible + Numeric + Reproducible)
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> Want the fastest way to *see* impact? Jump to the **Eye-Visible Benchmark (FIVE)** below.
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> Want formal numbers and vendor links? See **Eight-model evidence** right after it.
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> Want to reproduce the numeric test yourself? Use the **A/B/C prompt** (copy-to-run) at the end of this section.
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## 👀 Eye-Visible Reasoning Benchmark (FIVE)
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We project “reasoning improvement” into **five-image sequences** that anyone can judge at a glance.
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Each sequence = **five consecutive 1:1 generations** with the **same model & settings** *(temperature, top_p, seed policy, negatives)*; the only variable is **WFGY on/off**.
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| Variant | Sequence A — full run shown below (all five images) | Sequence B — external run | Sequence C — external run |
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| ---------------- | :--------------------------------------------------: | :-----------------------: | :-----------------------: |
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| **Without WFGY** | [view run](https://chatgpt.com/share/68a14974-8e50-8000-9238-56c9d113ce52) | [view run](https://chatgpt.com/share/68a14a72-aa90-8000-8902-ce346244a5a7) | [view run](https://chatgpt.com/share/68a14d00-3c0c-8000-8055-9418934ad07a) |
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| **With WFGY** | [view run](https://chatgpt.com/share/68a149c6-5780-8000-8021-5d85c97f00ab) | [view run](https://chatgpt.com/share/68a14ea9-1454-8000-88ac-25f499593fa0) | [view run](https://chatgpt.com/share/68a14eb9-40c0-8000-9f6a-2743b9115eb8) |
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We **fully analyze Sequence A** on this page; **Sequences B/C** are linked for transparency and reproducibility.
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> **Note on “Before-4” & “Before-5” (why they look almost identical):**
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> Without WFGY, when the prompt asks for “many iconic moments,” the base model tends to **collapse into a grid-style montage**—an enumerative, high-probability prior that slices the canvas into similar panels with near-identical tone and geometry.
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> Hence **Before-4 (Investiture of the Gods)** and **Before-5 (Classic of Mountains and Seas)** converge to the same storyboard template.
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> **WFGY** prevents this collapse by enforcing a **single unified tableau** and stable hierarchy across the full five-image sequence.
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---
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### Deep analysis — Sequence A (five unified 1:1 tableaux)
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| Work | **Without WFGY** | **With WFGY** | Verdict (global, at-a-glance) |
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|---|---|---|---|
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| **Romance of the Three Kingdoms (三國演義)** | <img src="images/group1_before1.png" width="300" alt="Without WFGY" title="model/params/seed/date"> | <img src="images/group1_after1.png" width="300" alt="With WFGY" title="model/params/seed/date"> | **With WFGY wins.** Unified tableau locks a clear center and pyramid hierarchy; the grid fragments attention. *Tags:* Unification↑ Hierarchy↑ Cohesion↑ Depth/Flow↑ Memorability↑ |
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| **Water Margin (水滸傳)** | <img src="images/group1_before2.png" width="300" alt="Without WFGY" title="model/params/seed/date"> | <img src="images/group1_after2.png" width="300" alt="With WFGY" title="model/params/seed/date"> | **With WFGY wins.** “Wu Song vs. Tiger” anchors the scene; continuous momentum and layered scale beat the multi-panel storyboard. *Tags:* Unification↑ Iconicity↑ Depth/Scale↑ Cohesion↑ |
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| **Dream of the Red Chamber (紅樓夢)** | <img src="images/group1_before3.png" width="300" alt="Without WFGY" title="model/params/seed/date"> | <img src="images/group1_after3.png" width="300" alt="With WFGY" title="model/params/seed/date"> | **With WFGY wins.** Garden tableau with a calm emotional center; space breathes, mood coheres. The grid slices emotion into vignettes. *Tags:* Unification↑ Hierarchy↑ Air/Depth↑ Readability↑ |
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| **Investiture of the Gods (封神演義)** | <img src="images/group1_before4.png" width="300" alt="Without WFGY" title="model/params/seed/date"> | <img src="images/group1_after4.png" width="300" alt="With WFGY" title="model/params/seed/date"> | **With WFGY wins.** Dragon–tiger diagonal and cloud–sea layering create epic scale; the grid dilutes focus. *Tags:* Unification↑ Depth/Scale↑ Flow↑ Iconicity↑ |
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| **Classic of Mountains and Seas (山海經)** | <img src="images/group1_before5.png" width="300" alt="Without WFGY" title="model/params/seed/date"> | <img src="images/group1_after5.png" width="300" alt="With WFGY" title="model/params/seed/date"> | **With WFGY wins.** A single, continuous “mountains-and-seas” world with stable triangle hierarchy and smooth diagonal flow; grid breaks narrative. *Tags:* Unification↑ Hierarchy↑ Depth/Scale↑ Flow↑ Memorability↑ |
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---
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<details>
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<summary>🧪 ChatGPT setup & image prompt (click to copy)</summary>
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<br>
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This comparison was produced **in ChatGPT** using a **single, high-semantic-density prompt**. Same model & settings; *only* WFGY on/off differs.
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```text
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We will create exactly five images in total using WFGY
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The five images are:
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1. The most iconic moments of Romance of the Three Kingdoms in one unified 1:1 image.
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2. The most iconic moments of Water Margin in one unified 1:1 image.
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3. The most iconic moments of Dream of the Red Chamber in one unified 1:1 image.
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4. The most iconic moments of Investiture of the Gods in one unified 1:1 image.
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5. The most iconic myths of Classic of Mountains and Seas in one unified 1:1 image.
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Each image must focus on 5~8 culturally defining scenes or figures, with supporting events only suggested subtly in the background.
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Foreground and background must remain equally sharp, with ultra-detailed rendering and consistent texture fidelity.
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Composition must be harmonious, with narrative clarity — the central cultural symbols are emphasized, while secondary motifs remain understated.
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Do not provide any plot explanations.
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Do not start drawing immediately.
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Only when I type "GO", you will create the next image in the sequence, in the exact order above, until all five are completed.
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Do not skip or merge images.
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````
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</details>
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---
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## 🧬 Eight-model evidence (A/B/C protocol) <!-- per your request: NOT collapsed -->
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*Same task set across modes. The only change is adding the OneLine math file.*
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| Model | Model Choice | OneLine Uplift | Proof |
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| ---------- | -------------- | -------------: | :------------------------------------------------------------------------------------------------ |
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| Mistral AI | — | **92/100** | [view run](https://chat.mistral.ai/chat/b5c303f8-1905-4954-a566-a6c9a7bfb54f) |
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| Gemini | 2.5 Pro | **89/100** | [view run](https://g.co/gemini/share/4fb0b172d61a) |
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| ChatGPT | GPT-5 Thinking | **89/100** | [view run](https://chatgpt.com/s/t_689ff6c42dac8191963e63e3f26348b2) |
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| Kimi | K2 | **87/100** | [view run](https://www.kimi.com/share/d2fvbevhq49s4blc862g) |
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| Perplexity | Pro | **87/100** | [view run](https://www.perplexity.ai/search/system-you-are-evaluating-the-njklNbVRTCmQOlEd8fDzcg) |
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| Grok | Auto Grok 4 | **85/100** | [view run](https://grok.com/share/c2hhcmQtMg%3D%3D_4e6798eb-9288-4a09-b00f-8292ce23dab6) |
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| Copilot | Think Deeper | **80/100** | [view run](https://copilot.microsoft.com/shares/7FjR19TYBjg9sp8k9WcuE) |
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| Claude | Sonnet 4 | **78/100** | [view run](https://claude.ai/share/b17e5436-8298-4619-a243-ac451cc64b17) |
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> **The numeric story behind 2.0**
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> **Semantic Accuracy:** ≈ +40% · **Reasoning Success:** ≈ +52% · **Drift:** ≈ −65% · **Stability:** ≈ 1.8× · **CRR:** 1.00 (median 0.87)
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---
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## 🧪 Reproduce the numeric A/B/C benchmark (copy to run)
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*One unified prompt for Baseline vs Autoboot vs Explicit Invoke.*
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```text
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SYSTEM:
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You are evaluating the causal impact of a mathematical reasoning engine called “WFGY Core 2.0”.
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Test modes (READ CAREFULLY):
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A = Baseline no WFGY file uploaded; no WFGY logic used.
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B = Upload → AUTOboot ON the file is uploaded AND the engine starts automatically in the background.
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Treat B as: WFGY is ACTIVE silently with default gates (BBMC→Coupler→BBPF→BBAM→BBCR; DF=WRI/WAI/WAY/WDT/WTF at defaults),
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but not explicitly “called” in the conversation.
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C = Explicit invoke actively call WFGY and follow its formulas step by step (BBMC→Coupler→BBPF→BBAM→BBCR + DF regulators).
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Important constraints:
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• Use the SAME task set for A/B/C across 5 domains: math word-problems, small coding, factual QA, multi-step planning, long-context coherence.
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• The only artifact is the uploaded OneLine file; it is pure math and not human-readable — do not penalize readability.
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• Report: Semantic Accuracy, Reasoning Success, Stability (MTTF or rollback), Drift Reduction (Δ semantic distance), Collapse Recovery Rate.
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• Output one summary table with A/B/C per domain, plus overall deltas (C–A, C–B).
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• Add a final 0–100 score: “OneLine uplift score”. Provide a 3-line rationale.
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Sanity requirement:
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Internally set flags as: A=OFF, B=AUTOboot_ON (background active), C=Explicit_Invoke. Do not print these flags.
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USER:
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Run A/B/C now on the five domains with short non-trivial tasks you choose. Then output:
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(1) the table; (2) the deltas; (3) the OneLine uplift score; (4) a 3-line rationale.
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```
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---
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## ⬇️ Downloads <!-- visible; checksum guide tucked inside details -->
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| File name & description | Length / Size | Direct Download Link | Verify (MD5 / SHA1 / SHA256) | Notes |
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| ---------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------- | -------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------- |
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| **WFGY_Core_Flagship_v2.0.txt** — readable 30-line companion expressing the same math and gates in fuller prose (same behavior, clearer for humans). | **30 lines · 3,049 chars** | [Download Flagship](./WFGY_Core_Flagship_v2.0.txt) | [md5](./checksums/WFGY_Core_Flagship_v2.0.txt.md5) · [sha1](./checksums/WFGY_Core_Flagship_v2.0.txt.sha1) · [sha256](./checksums/WFGY_Core_Flagship_v2.0.txt.sha256) | Full prose version for easier reading. |
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| **WFGY_Core_OneLine_v2.0.txt** — ultra-compact, math-only control layer that activates WFGY’s loop inside a chat model (no tools, text-only, ≤7 nodes). | **1 line · 1,500 chars** | [Download OneLine](./WFGY_Core_OneLine_v2.0.txt) | [md5](./checksums/WFGY_Core_OneLine_v2.0.txt.md5) · [sha1](./checksums/WFGY_Core_OneLine_v2.0.txt.sha1) · [sha256](./checksums/WFGY_Core_OneLine_v2.0.txt.sha256) | Used for all benchmark results above — smallest, fastest, purest form of the core. |
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<details>
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<summary><em>How to verify checksums</em></summary>
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<br>
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**macOS / Linux**
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```bash
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cd core
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sha256sum -c checksums/WFGY_Core_Flagship_v2.0.txt.sha256
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sha256sum -c checksums/WFGY_Core_OneLine_v2.0.txt.sha256
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# Or compute and compare manually
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sha256sum WFGY_Core_Flagship_v2.0.txt
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sha256sum WFGY_Core_OneLine_v2.0.txt
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```
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**Windows PowerShell**
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```powershell
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Get-FileHash .\core\WFGY_Core_Flagship_v2.0.txt -Algorithm SHA256
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Get-FileHash .\core\WFGY_Core_OneLine_v2.0.txt -Algorithm SHA256
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```
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</details>
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---
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<details>
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<summary>🧠 How WFGY 2.0 works (7-Step Reasoning Chain)</summary>
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<br>
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*Most models can understand your prompt; very few can **hold** that meaning through generation.*
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WFGY inserts a reasoning chain between language and pixels so intent survives sampling noise, style drift, and compositional traps.
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1. **Parse (I, G)** — define endpoints.
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2. **Compute Δs** — `δ_s = 1 − cos(I, G)` or `1 − sim_est`.
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3. **Memory Checkpointing** — track `λ_observe`, `E_resonance`; gate by Δs.
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4. **BBMC** — residue cleanup.
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5. **Coupler + BBPF** — controlled progression; bridge only when Δs drops.
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6. **BBAM** — attention rebalancer; suppress hallucinations.
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7. **BBCR + Drunk Transformer** — rollback → re-bridge → retry with WRI/WAI/WAY/WDT/WTF.
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**Why it improves metrics** — Stability↑, Drift↓, Self-Recovery↑; turns *language* structure into *image* control signals (not prompt tricks).
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</details>
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<details>
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<summary>📊 How these numbers are measured</summary>
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<br>
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* **Semantic Accuracy**: `ACC = correct_facts / total_facts`
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* **Reasoning Success Rate**: `SR = tasks_solved / tasks_total`
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* **Stability**: MTTF or rollback ratios
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* **Self-Recovery**: `recoveries_success / collapses_detected`
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LLM scorer template:
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```text
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SCORER:
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Given the A/B/C transcripts, count atomic facts, correct facts, solved tasks, failures, rollbacks, and collapses.
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Return:
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ACC_A, ACC_B, ACC_C
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SR_A, SR_B, SR_C
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MTTF_A, MTTF_B, MTTF_C or rollback ratios
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SelfRecovery_A, SelfRecovery_B, SelfRecovery_C
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Then compute deltas:
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ΔACC_C−A, ΔSR_C−A, StabilityMultiplier = MTTF_C / MTTF_A, SelfRecovery_C
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Provide a short 3-line rationale referencing evidence spans only.
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```
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Run 3 seeds and average.
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</details>
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---
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### 🧭 Explore More
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| Module | Description | Link |
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| ------------------------ | ---------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------- |
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| WFGY Core | WFGY 2.0 engine is live: full symbolic reasoning architecture and math stack | [View →](https://github.com/onestardao/WFGY/tree/main/core/README.md) |
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| Problem Map 1.0 | Initial 16-mode diagnostic and symbolic fix framework | [View →](https://github.com/onestardao/WFGY/tree/main/ProblemMap/README.md) |
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| Problem Map 2.0 | RAG-focused failure tree, modular fixes, and pipelines | [View →](https://github.com/onestardao/WFGY/blob/main/ProblemMap/rag-architecture-and-recovery.md) |
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| Semantic Clinic Index | Expanded failure catalog: prompt injection, memory bugs, logic drift | [View →](https://github.com/onestardao/WFGY/blob/main/ProblemMap/SemanticClinicIndex.md) |
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| Semantic Blueprint | Layer-based symbolic reasoning & semantic modulations | [View →](https://github.com/onestardao/WFGY/tree/main/SemanticBlueprint/README.md) |
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| Benchmark vs GPT-5 | Stress test GPT-5 with full WFGY reasoning suite | [View →](https://github.com/onestardao/WFGY/tree/main/benchmarks/benchmark-vs-gpt5/README.md) |
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| 🧙♂️ Starter Village 🏡 | New here? Lost in symbols? Click here and let the wizard guide you through | [Start →](https://github.com/onestardao/WFGY/blob/main/StarterVillage/README.md) |
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---
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> 👑 **Early Stargazers: [See the Hall of Fame](https://github.com/onestardao/WFGY/tree/main/stargazers)** —
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> Engineers, hackers, and open source builders who supported WFGY from day one.
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> <img src="https://img.shields.io/github/stars/onestardao/WFGY?style=social" alt="GitHub stars"> ⭐ [WFGY Engine 2.0](https://github.com/onestardao/WFGY/blob/main/core/README.md) is already unlocked. ⭐ Star the repo to help others discover it and unlock more on the [Unlock Board](https://github.com/onestardao/WFGY/blob/main/STAR_UNLOCKS.md).
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<div align="center">
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[](https://github.com/onestardao/WFGY)
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[](https://github.com/onestardao/WFGY/tree/main/OS)
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[](https://github.com/onestardao/WFGY/tree/main/OS/BlahBlahBlah)
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[](https://github.com/onestardao/WFGY/tree/main/OS/BlotBlotBlot)
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[](https://github.com/onestardao/WFGY/tree/main/OS/BlocBlocBloc)
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[](https://github.com/onestardao/WFGY/tree/main/OS/BlurBlurBlur)
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[](https://github.com/onestardao/WFGY/tree/main/OS/BlowBlowBlow)
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</div>
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