1. How does TXT OS store its “Semantic Tree Memory” between sessions?
2. When `kbtest` detects a hallucination, what happens next?
3. Any idea of the speed impact on smaller models like LLaMA-2-13B?
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?
1. How does TXT OS store its “Semantic Tree Memory” between sessions? 2. When `kbtest` detects a hallucination, what happens next? 3. Any idea of the speed impact on smaller models like LLaMA-2-13B?
Thanks for sharing—excited to try it out!