Maybe it all works, but the paper is not trivial to decipher and the GitHub repository does not seem to exist. It doesn't seem to define what are the inputs to the system (what is a query? UTF-8 text? tokens?) and what are the outputs. It'd really help if the algorithm was written out step by step with all the expected type information included.
At first I thought it was similar to something I've built before as a long-term slowly degrading cache for augmenting an FFN by caching well-learned answers, answering by performing a beam search in the key space resulting in located key accuracy measure (how well it corresponds to the input query) and answer confidence (has it been a long time since verification?), but that's not quite it? It feels similar in some way, but is it?
We handeld llm like a human brain we decopelled knowledge from the memory and build a memory layer that makes redoing things free and fast, so the llm can once it learned something solves it for free the next time
A cache remembers answers (only useful for the exact same question again). We remember the proven method and redo the work on every new question, so it solves ones it's never seen, which a cache simply can't.
We handeld llm like a human brain we decoupled knowledge from the memory and build a memory layer that makes redoing things free and fast, so the llm can once it learned something solves it for free the next time
+ odd page cuts, it's as-if no human has ever looked at this before uploading it.