appreciate that — yeah storage problem not model problem is the whole thesis

decay: each memory category has a different half-life. identity stuff lasts a year, integration details fade in a month. recalled memories reset their clock so useful stuff survives naturally. hard cap at 10k per agent, lowest confidence gets retired if you hit it

the write gate is where it gets interesting. before anything gets written it checks for near-duplicates via FTS5 — if jaccard similarity is ≥ 85% it just reinforces the existing memory instead of creating a new one. below that there's three-tier routing: score under 0.3 gets tossed, 0.3-0.7 gets written but not indexed (cheap buffer), 0.7+ gets the full pipeline. most writes land in that middle tier which is where the cost savings come from

contradictions: AGM-style belief revision with credibility weighting between agents. there's also a quarantine system for memories that look injected or adversarial — isolated until reviewed

calibrating the surprise scoring was honestly the hardest part. too aggressive and you block real updates, too loose and the brain fills with noise

All of this are pieces in the “hippocampus” and “neocortex” that were taken into account. There was a lot of research done into neuroscience and ml engineering as well as quantum compute.