Every "AI memory agent" I looked at before starting this does the same trick: embed every message, dump it in a vector database, retrieve the top-k most similar chunks at query time. That's not memory. That's a search index. It never forgets anything, it treats "nice weather today" with the same weight as "I'm allergic to penicillin," and it gets slower and dumber the longer it runs because retrieval gets noisier with every near-duplicate you never clean up.
For the Global AI Hackathon Series with Qwen Cloud (Track 1: MemoryAgent), the brief asked for three specific things: efficient storage and retrieval, timely forgetting of outdated information, and recalling critical memories within a limited context window. That middle one is the one almost nobody builds, because naive vector storage has no concept of "outdated." So I built it — real decay math, real consolidation, real contradiction detection — and then benchmarked it against the naive approach to prove it actually works, instead of just claiming it does.
This is the story of building that, on Qwen Cloud, in about two days, including the parts that broke.
The actual mechanism
Every memory Synapse stores gets a salience score that changes over time:
