The few scientific studies out there actually show a degradation of output quality when these markdown collections are fully LLM maintained (opposed to an increase when they’re human maintained), which I found fascinating.

I think the sweet spot is human curation of these documents, but unsupervised management is never the answer, especially if you don’t consciously think about debt / drift in these.

Are you referring to the one (1) study that showed that when cheaper LLM's auto-generated an AGENTS.md, it performed more poorly than human editted AGENTS.md? https://arxiv.org/abs/2602.11988

I'd love to see other sources that seek to academically understand how LLM's use context, specifically ones using modern frontier models.

My takeaway from these CLAUDE.md/AGENTS.md efforts isn't that agents can't maintain any form of context at all, rather, that bloated CLAUDE.md files filled with data that agents can gather on the spot very quickly are counter-productive.

For information which cannot be gathered on the spot quickly, clearly (to me) context helps improve quality, and in my experience, having AI summarize some key information in a thread and write to a file, and organize that, has been helpful and useful.

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