[Submitted on 1 Oct 2026]
Title:APDMem: Agent-Controlled Progressive Disclosure for Query-Adaptive Long-Term Memory
View a PDF of the paper titled APDMem: Agent-Controlled Progressive Disclosure for Query-Adaptive Long-Term Memory, by Chin-Lun Fu and 3 other authors
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Abstract:Personalized LLM assistants must recover sparse evidence from long conversation histories across queries of varying complexity. We introduce APDMem (Agent-controlled Progressive Disclosure Memory), a hierarchical long-term memory architecture that applies progressive disclosure to memory retrieval. Rather than relying on a flat memory store or fixed retrieval granularity, APDMem represents conversation history as four progressively detailed layers: thematic summaries, personalized key facts, turn-level evidence notes, and raw messages. At inference time, a controller applies progressive disclosure to the memory hierarchy: it first reads high-level summaries and drills into finer evidence only when needed. This creates an adaptive cost-fidelity trade-off: simple queries can terminate early, while complex temporal, multi-hop, or exact-evidence queries trigger deeper inspection. A note synthesizer converts retrieved evidence into a query-focused structure that consolidates facts, orders events, and flags contradictions before final answer generation. Experiments on LongMemEval show that APDMem achieves strong performance for long-context memory reasoning while accessing only 8% of the total conversations.
Comments: Accepted at EMNLP 2026 (Industry Track)
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
Cite as: arXiv:2610.02472 [cs.CL]
(or arXiv:2610.02472v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2610.02472
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Chin-Lun Fu [view email] [v1] Thu, 1 Oct 2026 20:51:45 UTC (758 KB)
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