onestardao

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 Y