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Researchers from University of Toronto and Vector Institute propose Segment Tree Memory (SegTreeMem), a memory architecture for long-horizon conversational agents that preserves temporal order using a hierarchical segment tree structure for both online construction and retrieval. Experiments across three datasets show nearly 20% improvement in LLM-judge accuracy over non-temporal tree baselines.