Exploring Cross-Scenario Generality of Agentic Memory Systems: Diagnostics and a Strong Baseline
Quick Answer
The study evaluates eight memory systems and an agentic harness across five scenarios, revealing that active control over storage and retrieval significantly enhances memory performance.
Quick Take
The AutoMEM harness demonstrated superior cross-scenario generality, outperforming existing designs tailored to single scenarios.
Key Points
- Eight memory systems were tested across five distinct scenarios.
- The AutoMEM harness achieved the best cross-task ranking.
- Active control over memory storage is crucial for performance.
- Existing designs are often limited to single scenario applications.
- The study highlights the need for generalizable memory systems in AI.
Paper Resources
Article Excerpt
From source RSS / original summaryarXiv:2606. 04315v1 Announce Type: new Abstract: agents accumulate histories that outgrow their context windows, motivating a growing literature on memory systems. Yet most existing designs are tuned to a single scenario (multi-session chat or a single trajectory format), and there is little evidence that they generalize across the heterogeneous trajectories agents encounter in deployment.
We revisit eight memory systems plus an agentic harness for search problems, on five scenarios: single-turn QA, multi-session chat, agentic-trajectory QA, memory stress tests, and long-horizon agentic tasks. The harness, which self-manages flat text-file storage via tool calls, achieves the best cross-task ranking, suggesting that memory performance hinges on giving the agent active control over storage and retrieval rather than on a passive store behind a fixed pipeline.
We instantiate this insight in AutoMEM, an agentic memory harness with a self-managed tool interface that achieves the best cross-scenario generality among the systems we evaluate.
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