The referenced CacheBlend paper (https://arxiv.org/pdf/2405.16444) which tries to stitch together multiple independent prefills looks more interesting and is new to me. The problem it's trying to solve is:
* KV projections for a given token at a given layer are a function of the residual at that layer,
* which is a function of the attention contribution of the previous layer,
* which is a (nonlinear) function of all earlier tokens' KV values at the previous layer.
This is what stops you from just pasting KV blocks together. Intuitively it might feel like you could do the equivalent of an MPEG deblocking filter to fix up the edges, but there's no guarantee the tokens that need fixing up are at the beginning of the KV block, so they have to be sneaky about it.
Unfortunately while that paper is quite verbose it's lacking in detail in the most important part: how they perform the approximate KV recomputation. It looks like the rough idea is that they fully recompute the KV for the first layer, and use the deviation between the recompute and the original cached KV as a heuristic for how important it is to recompute the full KV values (i.e. all remaining layers) for that token. They use that to derive a mask for the tokens which most strongly attend to the earlier context, then do a sparse update of those tokens.
What's still unclear is how this actually ends up being a performance win, given that the sparse update for each token still requires the exact KV for all the prior tokens in order to actually arrive at the correct value. It just kind of recurses the problem instead of fixing it. Maybe they just use the precomputed KV for the other tokens as input, and live with the approximation?
I think this is already somewhat pragmatically solved: just don't pull huge documents into context. Give the LLM tools to search them and retrieve the fragments that are actually relevant.
There are some transformation approaches to re-use the kv cache across inferences, but none are in wide use due to accuracy concerns following the transformation.
You basically have two agents look at the same cache under different views. Say agent_0 gets [a_1, a_0] and agent_1 gets [a_0, a_1]. They also write to this cache concurrently while decoding. To solve positional embedding inconsistencies they rotate the query projections for each block (a_0 and a_1) separately.
The computations you get that way do not exactly match the setup where you would naively prefill on every step, but are close enough.
Same trick could be used for the setup discussed here, I guess: prefill the document cache separately (p), prepend the system prompt (s) and get a cache view [s, p] from which you can then decode.
"People are asking the same questions and an answer is generated every time, what if we could like cache the questions and their answers..."
Sounds like someone was using chatgpt to understand how chatgpt works and then asked it to generate a paper based on his proposal to improve it.
When your abstract was clearly generated by an LLM and not curated to at least make it sound human, it does not make me want to read your paper.
For something gentler, 3Blue1Brown: https://www.youtube.com/watch?v=eMlx5fFNoYc (this is part of a series)
It reads like it started from an underspecification of "agents" x a strain of pop-wisdom about "KV cache" that I've seen enter mainstream discourse over the past 3 months that is Not Even Wrong, then, they solved a non-existent problem.
EDIT: based on the rest of comments either requesting a primer on terms, or, pointing out it makes errors in even more obvious ways, flagging.