Cognitive Memory for Agents: Vector Search vs Activation-Based Recall

I spent a few weeks trying to build an agent that could remember specific user preferences across sessions without bloating the context window to a point where latency became unbearable. The standard advice is always "just use a vector database." But as the memory store grew, I noticed a weird gap: the agent could find a document about "user prefers dark mode" via cosine similarity, but it couldn't "recall" the immediate emotional state or the nuance of the last three turns of conversation unles