What you call "Weathering" has been a constant gripe of mine. We have LLM-driven softwares toward that almost seem to start from scratch every time a request comes in - there are mechanisms to learn or generalize, like writing out a memory, but they are not reliable or reliable in general. There is no convenient lever to be able to say "yes this is in the memory but the request seems like it needs a fresh scan of data, so ignore your memory", or the opposite "you can infer this from stuff in the memory - don't re-reason!". There is some work like Dynamic Cheatsheets [1] and Agentic Context Engineering [2] that have studied this aspect, but we are far from a generally reliable solution. And till we have that, I think the system variations for systems trying to solve this problem are going to be (a) LLM-leaning: create unstructured memory files, with human in the loop as a filter to reject inaccurate responses (b) LLM-as-a-layer: what you describe and the article kind of is doing.
[1] https://aclanthology.org/2026.eacl-long.333/ [2] https://openreview.net/pdf?id=eC4ygDs02R