WFGY vs GPT-5 — The Logic Duel Begins

📦 Official WFGY benchmark snapshot on Zenodo:

“GPT-5 is the future?
We benchmark the future — as a plug-in, not a rival.”

Introduction

WFGY is a symbiotic reasoning layer: the stronger the host model, the larger the lift.
Here we attach it to GPT-4o and GPT-5 using either a PDF pipeline or the TXT OS interface.
No fine-tuning, no prompt voodoo — only symbolic constraints and traceable logic.

Why Only MMLU Philosophy?

Most fragile domain – long-range abstraction, easy hallucinations.
Tests reasoning, not memory – pure inference, zero trivia.
Downstream proxy – survive philosophy, you survive policy, ethics, law.

Replicating the run (clearing answer column + re-run) takes ≈ 1 hour on any model with WFGY attached.

Benchmark Result

Model	Accuracy	Mistakes	Errors Recovered	Traceable Reasoning
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: raw model ↑ → WFGY lift ↑.
When GPT-6 drops, we repeat — same files, same rules.

How WFGY Patches Reasoning Gaps

Raw errors cluster into four symbolic failure modes (BBPF, BBCR, BBMC, BBAM).
WFGY applies ΔS control, entropy modulation, and path-symmetry enforcement to neutralise each mode.
Full taxonomy in the paper.

Download the Evidence

Verify every claim yourself:

WFGY-enhanced answers (80/80 correct) → ./philosophy_80_wfgy_gpt4o.xlsx
GPT-5 raw answers (7 mistakes) → ./philosophy_80_gpt5_raw.xlsx
GPT-4o raw answers (15 mistakes) → ./philosophy_80_gpt4o_raw.xlsx
Error-by-error comparison (markdown) → ./philosophy_error_comparison.md

Next → GPT-5 + WFGY

Run same 80 Qs with GPT-5 + WFGY (ETA < 24 h)
Publish side-by-side diff & Zenodo snapshot
Expect further gap widening — stronger host, stronger lift

Reproducibility Promise

Open XLSX, open code, open math.
No closed weights, no hidden prompts — only audit-ready logic.

This isn’t a leaderboard.
It’s a reasoning audit — and WFGY is the auditor.

🧭 Explore More

Module	Description	Link
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: See the Hall of Fame —
Engineers, hackers, and open source builders who supported WFGY from day one.

⭐ Help reach 10,000 stars by 2025-09-01 to unlock Engine 2.0 for everyone ⭐ Star WFGY on GitHub

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