Your vector memory database remembers everything. That’s exactly the issue.

There is a design assumption baked into almost every vector database and AI memory implementation that sounds reasonable until you watch it grow nodes in production: that remembering more is always better. Through testing and refining our AUDN code, that is not exactly correct. After running VEKTOR Slipstream against real development sessions for 99 days, the database held 1,413 stored memories across four namespaces. Looking at the importance score distribution, 83 percent of those memories sat below 0.25 out of 1.0, what the system considers the noise floor. The remaining 17 percent, just 60 memories out of 1,413, sat above 0.75 and dominated every recall result. This is exactly what a curation layer is supposed to produce. Those 1,154 low-scored memories are accurate. They are not deleted. They are retrievable by direct query. What they are not is important enough to compete with the 60 high-signal entries every time the agent needs context. AUDN penalised them gradually over hundreds of writes because similar, more specific, or more frequently reinforced memories covered the same ground better. The system created a hierarchy. Without curation, all 1,413 memories would compete equally for every recall slot — and the agent would consistently surface redundant, lower-value context alongside the things that actually matter. That is what standard vector memory looks like without a curation layer. A slow, invisible degradation that nobody notices until the agent starts confidently giving you answers that are three months out of date. Every memory node in Vektor carries an importance score between 0 & 1. When a memory is first stored, it receives a score based on the content’s estimated significance. That score is not fixed. Every time a new memory arrives that is semantically related but not directly contradictory, the compatible verdict for that existing memory takes a small redundancy penalty. The penalty is intentionally modest: a factor based on how similar the in