Relevant But Forgotten
Your agent scores stored memories the way the Generative Agents memory stream does. Each of recency, importance and relevance is min-max normalized to across the candidate set, then summed with all weights equal to 1. Recency decays as where is hours since the memory was last retrieved. Importance is a 1-to-10 score the model assigned when the memory was written. Relevance is cosine similarity to the current query.
The user asks: "What should I feed Biscuit before the vet visit?"
| id | memory | hours since retrieval | importance | cosine |
|---|---|---|---|---|
| m1 | Biscuit is a corgi | 400 | 3 | 0.74 |
| m2 | Always reply in Spanish | 12 | 9 | 0.20 |
| m3 | Vet appointment for Biscuit on Friday | 30 | 6 | 0.71 |
| m4 | Prefers dark roast coffee | 900 | 2 | 0.18 |
| m5 | Biscuit is allergic to chicken | 210 | 7 | 0.66 |
Only three memories fit the budget. Which memory has the highest cosine similarity and still does not make the cut?
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