I’m an independent developer exploring whether a lightweight, open-source semantic reasoning kernel can significantly improve LLM alignment, robustness, and interpretability.

My system, WFGY (All Principles Return to One), wraps around existing language models and performs a “compress → validate → reconstruct” semantic cycle. In benchmarked tests, it yielded:

• 🔹 +42.1% multi-step reasoning accuracy
• 🔹 +22.4% semantic alignment improvements
• 🔹 3.6× increase in stability under ambiguous or adversarial prompts

Rather than relying on model scaling or fine-tuning, WFGY offers a reproducible pipeline that:

• Detects and corrects semantic drift
• Maintains structural coherence under long-horizon reasoning
• Is fully open-source, free, and requires no login or data collection
• Includes peer-reviewable documents and test cases hosted on Zenodo

Disclosures & Research Context

(For transparency: I’m the creator of WFGY, and I’ve published related semantic-physics experiments using this approach. Here, I aim to explore its feasibility and theoretical foundations in the context of alignment.)

Core Question to Discussion

Is it viable to use a semantic compression-and-reconstruction layer as a plugin for alignment — serving both as an interpretability tool and a guard against logical inconsistency?
What are the theoretical limitations, and how might this integrate with existing paradigms like modular alignment checkpoints or interpretability pipelines?

Links for Reference

GitHub: github.com/onestardao/WFGY