The memory system I am working on is specifically targeted at simulating human memory and retrieval patterns, including memory consolidation during sleep cycles. I would love to discuss the topic more with you - I'll look into getting access to your papers.

My research would be wildly out of date by now but I'm happy to chat. I probably have pre-corrections copies of some of the papers I could send you... and about a hundred citations I fetched back when I was doing this research.

Nice work! I have been thinking along similar lines and designed a simulation of human memory using a tiered database design with hot/warm/cold storage, temporal data, and graph relationship nodes. Hot memory is processed by an LLM during "sleep cycles" or downtime as I have seen others mention in the comments.

You have some novel approaches here which I have learned a lot from! Your hypershpere physics approach is fascinating - it's a different approach than I took, but it accomplishes some tasks without an LLM. Your importance-based eviction system can significantly reduce the size of the ephemeral session state before it gets processed to persistent memory by the LLM, and your half-life knowledge decay mechanism is more elegant than the temporal approach I took.

If I am finally allowed to post a show hn, I'll post a few details, but our projects mostly solve different things and are complimentary. I can certainly use some things in Hippo to improve my system, maybe there is something that would interest you in mine -- Memforge (https://github.com/salishforge/memforge) if you're interested.

Thank you for your work on this - it offers a pretty meaningful upgrade to some work I have been doing in AI agent memory management and enabling long-term context. I have it added to the backlog for integration!