So if I'm reading this right, it's basically a specialized embedding of a RAG, and then a model in the middle takes the agent's queries and returns responses in a form that is injecting into the main model's input token stream directly i.e. skipping actual text. And the injection format is INCREDIBLY token efficient. Sort of injecting a vibe rather than quotable text. Their results of "good on concepts, less good on quotes/exact text retrieval" seem to intuitively line up with that.
It looks like another difference with RAGs is that instead of reranking the doc vectors and taking the top 5 or so, this paper mixes all the docs (weighted by relevance) into a fixed number of "memory tokens".
It looks like this helps avoid a problem I've had with RAG - when a fact isn't local to a single patch of text.
> ...a problem I've had with RAG - when a fact isn't local to a single patch of text
But only in documents that make it through RAG’s hard cutoff. There’s an explanation of this on the related Stanford site at https://memory.stanford.edu/ (see “How MEMORILLA differs from RAG”)
I think its offering a compromise between accuracy and tokens. You don't have to use as many tokens and you get pretty good accuracy, but not the absolute best. I think, however, in many situations people aren't super concerned about the tokens as they are with the accuracy.
I am finding that semantic ~= lexical when you have something like a frontier model driving. Using full text search engines is still the most compelling approach for handling large document sets.
I really struggle with the semantic / embedding concerns when we have a generative model that can reverse engineer a Mario 64 rom dump to run on my toaster oven. These models know how to compose Lucene & other FTS query strings in such a way that we are effectively getting semantic search over purely lexical systems.
I think semantic embeddings are only a part of the picture. A hybrid system is definitely needed but only text based approaches are miserable. My agents keep on repeating the same mistakes across sessions. We need to first get rid of this notion of a session.