Suggestion: add WFGY 1.0 as a framework paper on LLM reliability and self-healing #2
Description
Hi, thanks for creating and maintaining PaperWeeklyAI.
It is a great way to study papers in computer vision, NLP, and machine learning every week.
I wanted to ask whether a framework-style paper on LLM reliability and self-healing would be in scope for this repo.
Project name: WFGY
Main paper DOI: 10.6084/m9.figshare.30338884
Main paper PDF: https://github.com/onestardao/WFGY/blob/main/I_am_not_lizardman/WFGY_All_Principles_Return_to_One_v1.0_PSBigBig_Public.pdf
GitHub repo: https://github.com/onestardao/WFGY
What WFGY 1.0 is
- A long-form framework paper that treats large language models as self-healing systems instead of static predictors.
- Focuses on detecting, isolating, and repairing reasoning failures when LLMs are used in real pipelines and applications.
- Provides concrete formulas, modules, and prompt workflows that readers can try directly with existing models.
- Published with a public DOI and backed by an open-source GitHub repository.
Why it might fit this project
- Many people who follow weekly AI papers are now interested in LLM robustness and debugging, not only in new architectures or bigger models.
- WFGY 1.0 offers a full-stack example of a framework paper in this area, which can be useful for readers who want to see how such work is structured.
- It can also serve as a reference when thinking about how to design more reliable experiments and systems around LLMs.
Possible way to integrate
- Add WFGY 1.0 to a week that focuses on LLM robustness / reliability / debugging, or to a small section that lists system/framework papers.
- Alternatively, mention it as an extra reading for people who want to dive deeper into the self-healing view of LLMs.
If this does not match the scope or style you want for PaperWeeklyAI, that is totally fine.
Thank you again for curating and sharing this resource.