If you have built agent memory on a vector store, you have probably watched recall quietly degrade as the store grows, and assumed you needed better embeddings or a bigger index.
We spent several months measuring that decay, and the result was not what we expected: LLM memory systems forget with the same mathematics as human memory, reproducing numbers from some of the most replicated experiments in clinical psychology. No tuning required to get there.
The dimensional lie
Start with the finding everything else follows from. Take an embedding model that advertises 384 or 1,024 dimensions and measure where the variance actually lives:
import numpy as np
