Chin-Lun Fu

Personalized assistants must recover sparse evidence from long conversation histories, while queries vary widely in complexity. APDMem organizes history into four layers, from thematic summaries and personalized key facts down to turn-level evidence notes and raw messages, and a controller reads high-level summaries first, drilling into finer evidence only when needed. On LongMemEval it achieves strong long-context memory reasoning while accessing only 8% of the total conversations.

Personalized assistants must recover sparse evidence from long conversation histories, while queries vary widely in complexity. APDMem organizes history into four layers, from thematic summaries and personalized key facts down to turn-level evidence notes and raw messages, and a controller reads high-level summaries first, drilling into finer evidence only when needed. On LongMemEval it achieves strong long-context memory reasoning while accessing only 8% of the total conversations.