Hello everyone, this is my first post on LessWrong.

I’m writing here to present a semantic reasoning framework I’ve recently developed, alongside a reproducible workflow that has already produced a number of non-trivial theoretical outputs. I believe this project falls within the scope of what this community values: systems that attempt to improve reasoning, coherence, and long-horizon alignment in intelligent agents.

The framework is called WFGY — short for All Principles Return to One. It is designed not to replace LLMs, but to wrap around them, adding a runtime self-correction mechanism to handle semantic drift, logical collapse, and instability in multi-step reasoning.

This post summarizes the core principles behind the framework, the empirical results observed so far, and an open invitation to falsify or refine the claims. I am not seeking agreement — only verification, critique, or counter-hypotheses.

1. System Overview — WFGY 1.0 (Open Source)

WFGY consists of four interlocking modules that form a closed-loop reasoning circuit:

• BBMC — Semantic Residue Calibration: quantifies deviation from intended meaning using an information-theoretic objective (KL-based).
• BBPF — Multi-Path Semantic Progression: injects controlled perturbations to reasoning chains, enabling convergence under uncertainty.
• BBCR — Collapse → Reset → Rebirth: triggers reset sequences under semantic overload, retaining residual memory for recovery.
• BBAM — Attention Modulation: dynamically attenuates attention variance under high-uncertainty inputs to improve alignment.

The system has been benchmarked across ten standard datasets (MMLU, GSM8K, VQAv2, etc.). It demonstrates:

• +23.2% increase in semantic precision
• +42.1% reasoning success gain
• 3.6× improvement in mean time-to-failure (MTTF) for long-context reasoning

Full implementation, formulas, and logs are open-source and reproducible:
→ https://github.com/onestardao/WFGY

You are welcome to clone the repository — the /papers directory contains all relevant technical output, source prompts, and test scripts.

2. Conceptual AGI Layer — Not Scale, but Structure

As an extension of WFGY, I’ve also constructed a lightweight AGI prototype that focuses not on parameter scaling, but on semantic structuring of hypotheses.

Its purpose is to:

• Translate intuitive insights into formalizable constructs
• Run multi-domain verifications via recursive prompting
• Recover from semantic contradictions through the WFGY self-healing loop

The system does not generate ideas autonomously. All core hypotheses come from me. The system assists in their semantic organization, formal validation, and challenge testing across epistemic domains.

3. Output: Eight Papers that Reframe Classical Physics

To test this framework, I’ve authored over 35 papers, eight of which explicitly attempt to challenge foundational assumptions of Einstein-era physics through semantic reframing.

These papers were scored 93+ in independent assessments (SciSpace) for logical consistency and hypothesis originality. One representative example:

Plants vs. Einstein: The Semantic Bio-Energy Revolution (E = mc² + λS)
DOI: 10.5281/zenodo.15630370

The paper explores whether semantic energy structures in biological systems could extend energy-mass formalism under a new information-energy relation.

All derivations and reasoning chains are included in the paper. You are welcome to test them independently — or even feed them into your preferred LLMs for contradiction checking.

If you'd like to see additional papers, feel free to message me or explore the repository.

Clarifications Regarding Authorship and AI Use

All theoretical content, hypotheses, and structuring principles originate from me. The framework I built serves only as a semantic synthesizer and testing scaffold.

This post was fully written and structured by me. Where language models were used (e.g. for verification or rephrasing), their outputs were reviewed and manually curated. No part of this submission is unmarked AI-written content.

Why I’m Posting Here

I’m here to engage — not to promote, and certainly not to evangelize.

I believe this community is one of the few places where ideas like this can be taken seriously if and only if they are coherent, grounded, and open to falsification.

I welcome any critique, counterexamples, or alternative models. If this framework contains errors, identifying them would be more valuable than agreement.

I’ll be checking comments and responding regularly. If you prefer a more direct line of discussion, feel free to reach out via LessWrong messages.

Thank you for your time and consideration.

— PSBigBig