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.

▲ NaiveEmpiricist 4 hours ago | parent | next [-]

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.

▲ CharlieDigital 2 hours ago | parent [-]

    > ...a problem I've had with RAG - when a fact isn't local to a single patch of text

Is this a problem, though? Vector similarity should surface the relevant, related patches.

	▲	mastry an hour ago \| parent [-]
		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”)

▲ johnshahbazian 4 hours ago | parent | prev [-]

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.