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📌 WFGY vs GPT-5 — The Logic Duel Begins
Upload the WFGY PDF (Zenodo DOI) 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 | ≈ 30 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
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.
Internal diff vs full MMLU ≤ ± 2 pts — good enough for a gut-check.
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.
B. 🧪 Full 80 Q MMLU-Philosophy Benchmark
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
| Module | Description | Link |
|---|---|---|
| WFGY Core | Standalone semantic reasoning engine for any LLM | View → |
| Problem Map 1.0 | Initial 16-mode diagnostic and symbolic fix framework | View → |
| Problem Map 2.0 | RAG-focused failure tree, modular fixes, and pipelines | View → |
| Semantic Clinic Index | Expanded failure catalog: prompt injection, memory bugs, logic drift | View → |
| Semantic Blueprint | Layer-based symbolic reasoning & semantic modulations | View → |
| Benchmark vs GPT-5 | Stress test GPT-5 with full WFGY reasoning suite | View → |
👑 Early Stargazers — Hall of Fame
Star the repo → help us hit 10 k by 2025-09-01 to unlock Engine 2.0!
