Basically, one wants to accumulate some kind of “state of the virtual world in question” as a memory while the story unfolds. Although, I can imagine that if the models start having “true long context” (e.g. long context without recall deterioration), and if that context is long enough to include the whole story, this might become unnecessary. So one might want to watch for emergence of those models (I think we are finally starting to see some tangible progress in this sense).

[-]ollie_6mo30

Thanks for your comment, I took a look at your example, but i'd say that is addressing a different issue - constrained output tokens, not ingestion of input tokens. I also wanted to avoid scaffolding approaches since i'm zero shotting, I don't want to use a chained series of prompts or chunking, I want to submit a single prompt.

I'm looking for any techniques similar to including an index of the prompt sections (like in a book with a list of the chapters) for the prompt and some character strings that differentiate the prompt's sections. Here's an example o... (read more)

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2mishka6mo

Ah, yes, you are right. And it's actually quite discouraging that because I thought that it was Gemini 2.5 Pro which was supposed to be the model which had finally mostly fixed the recall problems in the long context (if I remember correctly). So you seem to be saying that this recall depends much stronger on the nature of the input that one would infer from just briefly looking at published long-context benchmarks... That's useful to keep in mind.

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