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Partitioning agent memory by distinct life scenes rather than monolithic topic hierarchies eliminates retrieval interference and unlocks substantially higher accuracy in multi-hop personalization tasks.
Algorithmic mode collapse in ARLs reveals a critical gap between in-loop metric gains and real-world performance, but DAPS can bridge this divide effectively.