Currently constructing a repeatable test harness for PMB: Fixed task, with/without memory, repeated N times, giving number of tokens/turns/passed/not passed with a subjective quality score too. Would be happy to share the task set and evaluation criteria for testing on anyone else's memory server or clean slate control, not just mine.
That seems to be what most of these systems are doing: amplifying erros and hallucinations more than anything else.
Bloating: PMB does not inject anything into the store, just gets a small top-k relevant snippet for every task - normally a few hundred tokens, not an increasing dump from the store.
Poisoning is the one that is more interesting and your example with reconstruction proves the point that PMB does not have LLM on its read side. Human memory - and indeed any mechanism that uses paraphrasing while recalling information via the model - reconstructs the information on its own each time, and that's what makes it susceptible to manipulation and hallucination.
That which cannot be accomplished is to correct garbage in, where a lesson wrongly learned is faithfully recalled. Mitigations include the fact that everything is verbatim, source-stamped (by who, when, session), de-duped, recently decayed, and correctable, and all of which is displayed on a dashboard – making an error of recall detectable and auditable, rather than silently reconstructive drift. Detection of conflict/supersession is the next build.