I think this is a very important question, and it makes it clear that memory systems are less about fact retrieval, and more about knowledge classification. Memories systems are not document stores -- which to be fair this hippo system does recognize and motivates by exponential decay, recall strengthening and "sleep" consolidation.I personally don't think a memory system should try to "select what to forget", but to store everythign and live with the contradictions inherent in history. Having said that, we need to ascribe a certain confidence to each memory at storage time, where something uncertain is described as such, and when contradicting information gets stored, it reduces the confidence even further -- this on top of time decay and retreival bumps in confidence. E. T. Jaynes argued that this could be achieved in machines through Bayesian updating, say a beta distribution is stored for each memory and upon storing knowledge that confirms this memory, the beta distribution is updated to have more confidence (the original is the prior).
If every memory has a Bayesian prior denoting confidence, and this is surfaced when recalling, then the LLM itself can decide how to sythesize the different memories. Together with a "remembered on" field, the LLM can grok that the database schema was changed, or a certain design pattern was discarded (for example).
(Full disclosure, I have developed a memory system myself which I will post here in a couple days, with a slightly different target audience than hippo).