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.