Memini: Continual Learning through Associative Memory in LLM Systems

An LLM system can preserve every incoming document yet lose the ability to use earlier knowledge because new and old evidence compete within finite retrieval and context budgets. We introduce Memini, an external associative memory for continual learning from streams of unlabeled textual observations with all pretrained components held fixed. Drawing on episodic binding and cue-driven recall, Memini preserves each observation as an immutable source episode, while query-independent updates bind recurring concepts to episodes and accumulate source-attributable co-observations across them. At query time, partial cues follow bounded associative routes to recover complete source episodes, either as a standalone readout or in combination with dense retrieval. Reading does not modify persistent memory. On streams constructed from MuSiQue, 2WikiMultiHopQA, and HotpotQA, we evaluate acquisition, retention, and the joint use of evidence received at different updates. Under these schedules, Memini’s hybrid readout reduces mean answer-F1 forgetting by 50–72% relative to HippoRAG2 using its released incremental update path. It also improves cross-arrival answer F1 by 1.44–7.71 points over dense retrieval alone. Shuffling concept connections degrades retained answering even with the source archive unchanged, indicating that the organization of stored experience contributes beyond source availability. These results support source-grounded associative memory as a mechanism for system-level continual learning without adapting pretrained models.