Especially with Gemini Pro when providing long form textual references, providing many documents in a single context windows gives worse answers than having it summarize documents first, ask a question about the summary only, then provide the full text of the sub-documents on request (rag style or just simple agent loop).

Similarly I've personally noticed that Claude Code with Opus or Sonnet gets worse the more compactions happen, it's unclear to me whether it's just the summary gets worse, or if its the context window having a higher percentage of less relevant data, but even clearing the context and asking it to re-read the relevant files (even if they were mentioned and summarized in the compaction) gives better results.

Media literacy disclaimer: Chroma is a vectorDB company.

It's actually even more significant than it's possible to benchmark easily (though I'm glad this paper has done so.)

Truly useful LLM applications live at the boundaries of what the model can do. That is, attending to some aspect of the context that might be several logical "hops" away from the actual question or task.

I suspect that the context rot problem gets much worse for these more complex tasks... in fact, exponentially so for each logical "hop" which is required to answer successfully. Each hop compounds the "attention difficulty" which is increased by long/distracting contexts.