IMPRINT: Rethinking Retrieval for Long-Term Conversational Agent Memory

Long-term memory systems for conversational agents differ in how they organize memory but share one retrieval primitive: dense embeddings ranked by cosine similarity. We argue that this primitive, not the memory structure, is the bottleneck, because personal recall hinges on rare user-specific identifiers whose exact identity mean pooling averages away. We reframe recall as *imprint-following*, where each rare token is a trace pointing back to its source session, and instantiate it in **IMPRINT**, whose single *Token Profile Table* serves as an IDF-weighted inverted index and drives an online consolidation pool, with no retrieval training, no LLM calls at indexing, and no GPU at query time. We prove that rarity-weighted exact matching keeps a separation margin independent of the retrieval-unit length, whereas the dense margin decays as $L^{-1/2}$; the same result explains why session-level sparse retrieval was dismissed by prior work. On LoCoMo (27 sessions) and LongMemEval (500 sessions), IMPRINT beats the strongest prior memory system by up to +15.8 Recall@3 and +3.9 QA-F1, outperforms dense retrieval under an identical pipeline, and produces the best answers on LoCoMo.