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?
Can a semantic compression kernel like WFGY improve LLM alignment and institutional robustness?
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
Links for Reference
GitHub: github.com/onestardao/WFGY