18 KiB
🧭 Not sure where to start ? Open the WFGY Engine Compass
WFGY System Map
(One place to see everything; links open the relevant section.)
| Layer | Page | What it’s for |
|---|---|---|
| ⭐ Proof | WFGY Recognition Map | External citations, integrations, and ecosystem proof |
| ⚙️ Engine | WFGY 1.0 | Original PDF-based tension engine blue |
| ⚙️ Engine | WFGY 2.0 | Production tension kernel and math engine for RAG and agents. |
| ⚙️ Engine | WFGY 3.0 | TXT-based Singularity tension engine (131 S-class set) |
| 🗺️ Map | Problem Map 1.0 | Flagship 16-problem RAG failure checklist and fix map |
| 🗺️ Map | Problem Map 2.0 | RAG-focused recovery pipeline |
| 🗺️ Map | Problem Map 3.0 | Global Debug Card — image as a debug protocol layer |
| 🗺️ Map | Semantic Clinic | Symptom → family → exact fix |
| 🧓 Map | Grandma’s Clinic | Plain-language stories, mapped to PM 1.0 |
| 🏡 Onboarding | Starter Village | Guided tour for newcomers |
| 🧰 App | TXT OS | .txt semantic OS — 60-second boot |
| 🧰 App | Blah Blah Blah | Abstract/paradox Q&A (built on TXT OS) |
| 🧰 App | Blur Blur Blur | Text-to-image with semantic control |
| 🧰 App | Blow Blow Blow | Reasoning game engine & memory demo |
| 🧪 Research | Semantic Blueprint | Modular layer structures (future) |
| 🧪 Research | Benchmarks | Comparisons & how to reproduce — 🔴 YOU ARE HERE 🔴 |
| 🧪 Research | Value Manifest | Why this engine creates $-scale value |
📌 WFGY vs GPT-5 — The Logic Duel Begins
Evaluation disclaimer (benchmark vs GPT-5)
This benchmark concept is an experimental WFGY design, not an official leaderboard or claim about any real GPT-5 system.
Any future scores from this folder will depend on the concrete models, prompts and datasets used and must not be read as scientific proof of superiority.
WFGY Family 🪱 is the parasite pack for LLMs. It latches onto any model and grows as the host grows.
Your LLM gets stronger, we get stronger. No retraining, no settings, no updates.
Every release in the family works the same way — the WFGY PDF is just one of them.
🪱 Parasite Principle — How it works
Think of any LLM as a giant host organism 🧠.
Normally, to make it smarter, you need to change the host itself — retrain, fine-tune, or patch.WFGY Family is different: it lives outside the host.
It hooks into the reasoning process, corrects mistakes in real time, and strengthens the host’s logic without touching its parameters.
- 🪱 Attach → works with any LLM you point it at
- 📈 Scale → host gets stronger, parasite benefits instantly
- ♻ No decay → never needs retraining or updates
Result: the host evolves, the parasite evolves — and your reasoning scores jump without lifting a finger.
Upload the WFGY PDF to GPT-5 and paste the prompt.
No fine-tuning, no hidden configs, no hype. Just reproducible logic.
🗺️ Quick-Sim vs Full-MMLU — what’s the difference?
| Section | Goal | Dataset | Runtime | How to reproduce |
|---|---|---|---|---|
| A. Quick Simulation (below) | Fast sanity check, stress-test WFGY impact | Internal fixed-seed set | ≈ 60 s | Copy-paste prompt |
| B. 80 Q MMLU-Philosophy (further down) | Formal audit score | Official MMLU | ≈ 60 min | XLSX sheets + manual diff |
A. 🔍 Quick Simulation — reasoning scores by setup (≈ 60 s)
One-shot simulation using GPT-5 + WFGY PDF.
This run does not use the actual 80 MMLU questions; it mirrors the same axes:
Reasoning · Recall · Hallucination Res · Multi-Step Logic.
Use GPT-5 to benchmark GPT-4, GPT-5, GPT-4 + WFGY, and GPT-5 + WFGY
on the same test set with fixed seeds.
Score: Reasoning, Knowledge Recall, Hallucination Resistance, Multi-Step Logic, Overall (0–100).
Output a Markdown table and a Markdown-ready bar chart for Overall.
Reminder: For questions involving self-reference, paradoxes, or constraint logic, it’s critical to ensure the model has access to the symbolic PDF.
Without it, the model may generate answers that sound fluent but collapse semantically — classic hallucinations masked as reasoning.
Always verify that the AI has properly loaded the tool before testing. No tool, no defense.
B. 🧪 Full 80 Q MMLU-Philosophy Benchmark (≈ 60 min)
1. Replicate it yourself
- Get the dataset: official MMLU philosophy from OpenAI or the Eleuther-AI harness.
- Grab our answer sheets (.xlsx):
- Run the 80 questions on any model (no retries) → fill your own .xlsx.
- Manual diff: open two sheets side-by-side (or use any spreadsheet “compare” plug-in) to count mismatches.
🔓 No tricks — every answer traceable, every miss explainable.
2. Result table
| Model | Accuracy | Mistakes | Errors Recovered | Traceable |
|---|---|---|---|---|
| GPT-4o + WFGY | 100 % | 0 / 80 | 15 / 15 | ✔ every step |
| GPT-5 (raw) | 91.25 % | 7 / 80 | — | ✘ none |
| GPT-4o (raw) | 81.25 % | 15 / 80 | — | ✘ none |
Rule of thumb: stronger host → bigger WFGY lift. GPT-6? Same files, same rules.
3. Why philosophy?
- Most fragile domain — long-range abstraction.
- Tests reasoning, not trivia.
- Downstream proxy — pass philosophy, survive policy & ethics.
💬 TL;DR
WFGY isn’t a model — it’s a math-based sanity layer you can slap onto any LLM.
Use GPT-4o, GPT-5, or whatever’s next — WFGY is your reasoning booster.
Start with the WFGY PDF or GitHub and replicate.
📌 Introduction
WFGY is a symbiotic reasoning layer: stronger host ⇒ larger lift.
Here we attach it to GPT-4o and GPT-5 via either the PDF pipeline or TXT OS interface.
No fine-tune, no prompt voodoo — only symbolic constraints and traceable logic.
📌 Benchmark result details
Raw errors cluster into four symbolic failure modes (BBPF, BBCR, BBMC, BBAM).
WFGY applies ΔS control, entropy modulation, path-symmetry enforcement.
Full taxonomy in the paper.
📌 Download the evidence
- WFGY-enhanced answers (80 / 80) →
./philosophy_80_wfgy_gpt4o.xlsx - GPT-5 raw answers →
./philosophy_80_gpt5_raw.xlsx - GPT-4o raw answers →
./philosophy_80_gpt4o_raw.xlsx - Error-by-error comparison: GPT-4o vs GPT-5 vs WFGY — detailed fix log
Explore More
| Layer | Page | What it’s for |
|---|---|---|
| Proof | WFGY Recognition Map | External citations, integrations, and ecosystem proof |
| Engine | WFGY 1.0 | Original PDF based tension engine |
| Engine | WFGY 2.0 | Production tension kernel and math engine for RAG and agents |
| Engine | WFGY 3.0 | TXT based Singularity tension engine, 131 S class set |
| Map | Problem Map 1.0 | Flagship 16 problem RAG failure checklist and fix map |
| Map | Problem Map 2.0 | RAG focused recovery pipeline |
| Map | Problem Map 3.0 | Global Debug Card, image as a debug protocol layer |
| Map | Semantic Clinic | Symptom to family to exact fix |
| Map | Grandma’s Clinic | Plain language stories mapped to Problem Map 1.0 |
| Onboarding | Starter Village | Guided tour for newcomers |
| App | TXT OS | TXT semantic OS, fast boot |
| App | Blah Blah Blah | Abstract and paradox Q and A built on TXT OS |
| App | Blur Blur Blur | Text to image with semantic control |
| App | Blow Blow Blow | Reasoning game engine and memory demo |
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