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.
