Agent-Native Memory

• The Top AI Papers of the Week (June 21 - 28)

Memory for LLM agents has quietly grown from a retrieval add-on into a full data system, with persistent storage, retrieval, update, consolidation, and lifecycle governance running throughout an agent’s execution. Yet most evaluations still score memory only through end-to-end task metrics like F1 and BLEU, treating the whole stack as a black box. This paper studies agent memory from a data management perspective and asks what we are actually missing when we measure it that way.

• A data management view of memory: The authors argue that operational cost, architectural trade-offs across memory modules, and robustness under dynamic knowledge updates are first-class concerns that task-success metrics hide entirely.
• A four-module decomposition: They break memory into representation and storage, extraction, retrieval and routing, and maintenance, then evaluate 12 representative memory systems plus two baselines across five workloads spanning 11 datasets.
• No single architecture wins: Effectiveness depends on how well the memory structure matches the workload bottleneck, and fine-grained ablations quantify each module’s effect on representation fidelity, retrieval precision, update correctness, and long-horizon stability.
• Why it matters: The study shows localized maintenance is more cost-efficient than global reorganization, and reframing memory as a system with measurable trade-offs is what gets us toward genuinely agent-native memory rather than another leaderboard number.