Same memory, different model. Why do local 8B models use memory worse?

I’ve been building FERNme, an open-source, brain-inspired memory engine for AI agents.

While testing, I noticed something interesting. With the same FERNme memory, graph, and retrieval pipeline, a stronger API reasoning model performed very well in my initial tests, while a lightweight local 8B model occasionally made mistakes. The memory itself didn’t change, only the reasoning model did.

This made me think memory and reasoning are separate problems. Human memory also isn’t useful just because something is stored. We use context and reasoning to decide which memories matter in a situation.
FERNme exposes signals like strength, salience, uncertainty, provenance, age, contradictions, and related memories. But the model still has to interpret those signals correctly.

So I’m now experimenting with an agent layer on top of FERNme to help smaller local models retrieve and reason over memory more effectively, while keeping the memory engine model-agnostic.
For people building local AI agents: have you seen similar behavior? Would you focus on improving the memory engine itself, adding an agent layer over retrieval, or using more structured prompting / deterministic steps to help smaller models interpret memory better?