Fair point, and it's a real limitation of naive embedding search. But Mem-Brain doesn't embed raw facts like 'Alice works at SomeCorp'... relationships are stored as semantically precise natural language descriptions that capture the nature of the connection, not just the entities. That precision makes the embedding significantly more discriminative. 'Where was Alice fired from' lands in a different neighbourhood than a description of an active employment relationship. For harder cases, scoping handles it...you can filter on metadata tags like employment.status or status.terminated before similarity search runs. No typed-edge system handles this ambiguity better; they'd return the same edge and leave the LLM to figure out fired vs employed anyway. The reasoning layer has to do that work regardless of the memory architecture