Cued Recall: Making Long-Term Memory Accessible with Relationally Conditioned Cues

Long-term memory systems for large language model (LLM) agents typically match a query against the event's own content. This practice creates a representation asymmetry between query formulation and memory storage. Anticipated-question generation can narrow this gap when stored information is expressed in query-like forms, but content-only generation remains tied to the event's own wording and may miss questions whose answers depend on its relational context. We propose Cued Recall, a memory framework that grounds anticipated-question generation in local relational structure and aligns memory representations with the diverse ways future queries may access them. Specifically, Cued Recall first constructs a relation-labeled event graph. It links events through semantic, keyword, temporal, participant, and causal relations. Then, for each event, an LLM generates anticipated questions conditioned on its local relational neighborhood. Each question is grounded in information supported by the event and its context. At query time, the cues are combined with event content to identify seed events, and relation-aware graph activation retrieves supporting evidence around those seeds. On LoCoMo, the removal of generated cues causes a 12.20% relative drop in F1. A controlled comparison shows that cue generation grounded in relational context, rather than in the event's content alone, improves F1 by 6.89 points overall and by 10.89 points on multi-hop questions. Cued Recall further achieves an average F1 of 69.37 on HotPotQA and an average accuracy of 62.31% on PersonaMem.