Reading/resource suggestion: WFGY 1.0 (math + reasoning structure for LLM robustness)

[onestardao]

Hi maintainer,

Not sure if you accept external resources, but I think this could fit as an extra reading item for ML + reasoning systems:

WFGY 1.0 PDF (MIT):
https://github.com/onestardao/WFGY/blob/main/I_am_not_lizardman/WFGY_All_Principles_Return_to_One_v1.0_PSBigBig_Public.pdf

It’s a “math + structure” approach to make LLM outputs more stable and debuggable (especially for tool/RAG style pipelines).
Related practical checklist (16 common RAG/agent failures):
https://github.com/onestardao/WFGY/tree/main/ProblemMap

If there is a better place/section, please let me know. Thanks.

[onestardao]

Hi, just a brief follow-up here.

Since opening this issue, the WFGY approach has evolved more clearly into two layers:

• WFGY 1.0 → math + reasoning structure (robustness perspective)
• WFGY 2.0 → a structured “RAG 16 Problem Map” for failure taxonomy

The 2.0 layer has now been adopted or referenced by 20+ repositories in the RAG / LLM ecosystem (including RAGFlow, LlamaIndex, ToolUniverse, Rankify, and Multimodal RAG Survey), mainly as a structured debugging vocabulary rather than a competing framework.

From a “homemade ML” or learning perspective, the part that may be most relevant here is the reasoning-structure angle — treating LLM systems as layered pipelines with explicit failure modes instead of black-box outputs.

If this fits better under robustness / evaluation / reasoning resources, I’m happy to adjust the framing or submit a minimal PR accordingly.

If it’s outside scope, totally understandable.

Thanks again.