Note: English isn’t my native language, so I’ve used AI to help write this post and communicate my ideas more clearly. Thanks for your understanding!
A Public Semantic Kernel for LLM Reasoning – WFGY 1.0
Hi, I’m PSBigBig.
WFGY (“All Principles Return to One”) is a semantic reasoning kernel I developed to address fragmentation, instability, and contradiction in large language models.
Rather than more theory, this is a hands-on invitation to test a real toolkit. WFGY is designed to reduce ambiguity and contradiction in AI reasoning — and it works with your existing models, no retraining needed.
🔬 Key Results After Applying WFGY
- Semantic accuracy: +22.4%
- Reasoning success rate: +42.1%
- Output stability: 3.6× improvement (measured by prompt tests across disciplines and complexity levels)
All improvements come without retraining the model. No fine-tuning — just semantic interpretation.
🧪 Try It Yourself (No Registration Required)
Step 1 – Download the Core Paper
→ https://zenodo.org/records/15630969
No login, no tracking — just grab the PDF.
Step 2 – Upload It to Any AI
Most modern AIs let you upload PDFs. Just give the model the WFGY 1.0 paper, then prompt:
Please use WFGY to interpret this question: [your question]
Step 3 – Observe the Shift
You’ll notice fewer contradictions, more structured inference, and a tendency to repair earlier gaps. It’s not magic — just portable semantic structure.
🔓 100% Free, Open, and Barrier-Free
- 100% open source
- No signup needed
- No personal data required
- Free forever
Source code: https://github.com/onestardao/WFGY
🧠 More Prompts + Test Suites
To unlock WFGY’s full potential, check out our dedicated prompt pack:
→ https://zenodo.org/records/15657016
It includes evaluation templates, semantic stress tests, and a few hidden surprises.
This is a public release.
If you find WFGY useful, I’d love to hear your thoughts — and if not, that’s fine too. Either way, thanks for exploring!
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welcome any one to leave comment to discuss