SUE-Mem: Logic-Guided Overlapping Event Storylines for Long-Term Conversational Memory

Long-term dialogue agents must retrieve facts across sessions while distinguishing event relationships from topical similarity. We introduce SUE-Mem, a memory framework that organizes temporally grounded atomic events into overlapping storylines. For each new event, SUE-Mem retrieves candidate storyline summaries and uses an LLM judge with historical event evidence to assess membership. An event may join multiple independently evolving storylines, with milestone-triggered checks identifying those that should split. At query time, a hybrid retriever protects highly ranked semantic candidates and selects additional storylines using semantic, lexical, and exact-anchor scores. On LoCoMo, SUE-Mem achieves absolute gains of 14.53% in Multi-Hop and 5.59% in Overall Judge score over the strongest reported baselines. On LongMemEval, it achieves Overall F1 and Judge scores of 46.35 and 71.32. Ablations support the contributions of overlapping assignment, event-level evidence, and adaptive splitting to long-term conversational question answering.