I went through the structure and found the semantic correction idea pretty intriguing.
Can you explain a bit more about how WFGY actually achieves such improvements in reasoning and stability?
Specifically, what makes it different from just engineering better prompts or using more advanced LLMs?
Can you explain a bit more about how WFGY actually achieves such improvements in reasoning and stability? Specifically, what makes it different from just engineering better prompts or using more advanced LLMs?