Proposal: Use semantic compression as input diffusion to read sessions larger than the context window [R]

I've been trying to come up with a solution for keeping extremely long ai sessions coherent. Sometimes there is too much substance to risk compaction. With so much buzz around diffusion going on it got me thinking, what if we treat the context like a progressive render, blurry>sharp. The practical way to make text "blurry" is compression. This is a "diffusion inspired" system which borrows the coarse-to-fine process, not the formal math. It uses semantic compression so the overall structure of the session stays intact. Read the compressed version first to build an outline. Then read progressively less compressed slices until you're reading small verbatim chunks that give full detail. So you're basically using compression as noise on the input side, then progressively building an output. Each slice is compressed to fit within the context window, so the model only ever needs to read the current slice+input+current output. Tell the model what pass it's on, so it knows whether to write an outline or add detail. The thing I'm actually trying to preserve is what you'd call "non-local information". Think of it as stuff that surfaces when looking at the whole session & doesn't survive fragmented retrieval. Retrieval misses it, compaction deletes it. Both miss what only exists in a holistic view. Here is a visual demonstration to get a general idea of the workflow. https://dev-boz.github.io/diffusive-semantic-compression/demo/architecture-demo.html There is substantial overlap with lots of prior art, Recursive Language Models is one of the closest (source and output on disk, process recursively). I wrote most of this before I found RLM and nearly gave up before realising there was still a small part that was novel. As far as I can tell there's no exact match for this particular implementation. Please let me know if I've missed one. The difference to regular masked diffusion is in changing the length of the input rather than just masking. What seems to be new ground is using