Are We Ready For an Agent-Native Memory System? SJTU Benchmarks 12 Architectures
A systematic evaluation of AI agent memory through a data-management lens from SJTU and Tsinghua. The paper proposes a framework decomposing agent memory into four modules — representation and storage, extraction, retrieval and routing, and maintenance — then benchmarks 12 existing memory systems. Key finding: no single architecture performs optimally across all workloads; localized maintenance is more cost-efficient than full reorganization.
Why it matters
As agentic AI proliferates, memory is increasingly a deployment bottleneck. This is the first systematic benchmark across 12 memory architectures using a unified framework, giving practitioners a principled basis for architecture selection. Ranked second on HF Daily Papers for June 25 (40 upvotes).
Importance: 2/5
First systematic multi-architecture memory benchmark; directly actionable for agent system designers