I’m the creator of WFGY (All Principles Return to One) — a semantic reasoning framework designed to enhance the stability, precision, and self-correction capacity of large language models.
WFGY 1.0, released open-source on June 15, improves reasoning reliability by:
+42.1% semantic alignment accuracy
+22.4% meaning-level consistency
3.6× improvement in logical stability under complex prompting
The system includes an SDK (pip install wfgy
) and a public GitHub repository containing test suites, benchmarks, and papers demonstrating its potential for semantic alignment and AI-aided epistemology.
→ https://github.com/onestardao/WFGY
I’m currently inviting independent evaluation, collaboration, and critical feedback from researchers working on AGI safety, interpretability, or reasoning architectures.
Happy to clarify any part of the technical assumptions or test cases.
If you’re working on reasoning alignment or AGI epistemics, I’d love your thoughts.