That calls lost-in-middle issue with LLM models. You have to use appropriate workflow patterns like map/reduce to handle big context. But be sure to run experiments on dataset first to understand what logic works well and stable. Where NOT! And how it can be combined to reduce LLM stohastic problems in prod

DeepSeek v4.1 flash seems to do this thing where it starts saying that we should wrap up the current work and start again "next session" (whatever that means), that the "context budget" is almost up, or some other wording I'm forgetting, as the context gets longer.

I've taken to just manually making it write a handoff file and clearing context completely. I'm pretty much manually compacting instead of my harness doing it. Feels weird, but it does work.

I can't cite the sources at the moment but this is already well known - a few papers on it and Anthropic say somewhere in their docs that you want to get across the minimum amount of context required to achieve your goal. Quality diminishes the more you dump in.

Let's bump that sample size to 2. I've observed the same and that observation has heavily influenced the design of my coding agent harness.

It does, and, when combined with Claude's stingy cache TTL of 5 minutes (unless you up it to 1h at a sizeable increase in token cost) for API billing, means that some sessions cost like 10x what they should because of cold caches.