AutoMEM achieves best cross-scenario memory generality in agent benchmark
A paper by Zhikai Chen, Jialiang Gu, and Junyu Yin benchmarks eight memory systems across five agent scenarios and introduces AutoMEM, an agentic memory harness with a self-managed tool interface that achieves the best cross-scenario generality.
Score breakdown
The paper identifies that active agent control over memory storage and retrieval — rather than passive, pipeline-fixed stores — is the key driver of cross-scenario generality, a finding that directly informs how memory systems for deployed LLM agents should be designed.
- 01Eight existing memory systems plus an agentic harness are evaluated across five scenarios.
- 02The five scenarios are: single-turn QA, multi-session chat, agentic-trajectory QA, memory stress tests, and long-horizon agentic tasks.
- 03Most existing memory designs are tuned to a single scenario and show limited cross-scenario generalization.
A paper from Zhikai Chen, Jialiang Gu, and Junyu Yin revisits the landscape of LLM agent memory systems, motivated by the core problem that agent histories routinely outgrow context windows. The authors identify a critical gap in the existing literature: most memory designs are tuned to a single scenario — typically multi-session chat or a single trajectory format — and there is little evidence they generalize to the heterogeneous trajectories agents encounter in real deployment.
The agentic harness, which self-manages flat text-file storage via tool calls, achieves the best cross-task ranking among all evaluated systems.
To diagnose this, the authors benchmark eight memory systems plus an agentic harness for search problems across five distinct scenarios: single-turn QA, multi-session chat, agentic-trajectory QA, memory stress tests, and long-horizon agentic tasks. The agentic harness, which self-manages flat text-file storage via tool calls, achieves the best cross-task ranking among all evaluated systems.
The central insight drawn from this evaluation is that memory performance depends on giving the agent active control over storage and retrieval, rather than on a passive store behind a fixed pipeline. The authors instantiate this principle in AutoMEM, an agentic memory harness with a self-managed tool interface, which achieves the best cross-scenario generality among all systems evaluated in the study.
Key facts
- 01Eight existing memory systems plus an agentic harness are evaluated across five scenarios.
- 02The five scenarios are: single-turn QA, multi-session chat, agentic-trajectory QA, memory stress tests, and long-horizon agentic tasks.
- 03Most existing memory designs are tuned to a single scenario and show limited cross-scenario generalization.
- 04An agentic harness that self-manages flat text-file storage via tool calls achieves the best cross-task ranking.
- 05The key finding is that memory performance hinges on active agent control over storage and retrieval, not a passive fixed pipeline.
- 06AutoMEM is introduced as an agentic memory harness with a self-managed tool interface.
- 07AutoMEM achieves the best cross-scenario generality among all evaluated systems.
Topics
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