Could you use this to build a proactive memory formation and retrieval system for LLMs that runs lightning fast?

Last 32k of connect + Summary of current task: Did we learn something useful here (true/false)? What is the category to file it under? Then notify the LLM to file it away.

What class of memory might be useful here? Model gives probability to each item in the list. Short description of all memories ordered by tagged class is used in the next round. Are any of these memories useful in the current context, such that they will inform the model and help in its task (yes/no)?

I’m sure there’s some fine tuning to be had, but this sure seems like the basis for a substantially better proactive memory system that works around an existing LLM conversation.

If I’m understanding what this does and how this works (generic input, intelligent classification with probabilities, rapid and cheap), this is absolutely nuts.