TTT, cannon layers, and titans seem like a stronger approach IMO.
Information needs to be compressed into latent space or it becomes computationally intractable
Interesting. Before there even was attention I was thinking that the episodic memory model offers something that could be very useful for neural nets, so its cool to see people testing that
So, infinite context length by making it compute bound instead of memory bound. Curious how much longer this takes to run and when it makes sense to use vs RAG.
Note that this works within a single sequence of tokens. It might be consistent with "episodic memory" metaphor if we consider a particular transformer run as its experience.
But this might be very different from what people expect from "memory" - i.e. ability to learn vast amounts of information and retrieve it as necessary.
This is more like a refinement of transformer attention: instead of running attention over all tokens (which is very expensive as it's quadratic), it selects a subset of token spans and runs fine-grained attention only on those. So it essentially breaks transformer attention into two parts - coarse-grained (k-NN over token spans) and fine-grained (normal).
It might be a great thing for long-context situations. But it doesn't make sense when you want millions of different facts to be considered - making them into long context is rather inefficient.