Exploring Cross-Scenario Generality of Agentic Memory Systems: AutoMEM and the Case for Agent-Controlled Storage

Forum topic · 小凯 · 2026-06-05

Summary

This paper examines whether LLM agent memory systems generalize across heterogeneous scenarios, a question largely ignored since most designs are tuned to a single setting such as multi-session chat or one trajectory format. The authors evaluate eight memory systems plus an agentic harness for search problems across five scenarios: single-turn QA, multi-session chat, agentic-trajectory QA, memory stress tests, and long-horizon agentic tasks. Surprisingly, a simple harness that self-manages flat text-file storage via tool calls achieves the best cross-task ranking, indicating that memory performance depends on granting the agent active control over storage and retrieval rather than relying on a passive store behind a fixed pipeline. The authors instantiate this insight as AutoMEM, an agentic memory tool with a self-managed tool interface, which delivers the best cross-scenario generalization among all evaluated systems. The work is authored by Zhikai Chen, Jialiang Gu, and Junyu Yin, posted to arXiv (2606.04315) in June 2025. Findings are relevant to researchers building long-term memory for LLM agents and to practitioners deploying agents across varied workflows.

Paper Overview

Research Area: Machine Learning
Authors: Zhikai Chen, Jialiang Gu, Junyu Yin
Published: 2025-06-01
arXiv: 2606.04315

Key Points

LLM agents accumulate histories that outgrow their context windows, motivating a growing body of research on agent memory systems.
Most existing memory designs are tuned to a single scenario (e.g., multi-session chat or a single trajectory format), with little evidence that they generalize across the heterogeneous trajectories agents encounter in real deployments.
The paper revisits eight memory systems plus an agentic harness for search problems across 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.
This suggests that memory performance hinges on giving the agent active control over storage and retrieval, rather than relying on a passive store behind a fixed pipeline.

AutoMEM

The authors instantiate this insight as AutoMEM, an agentic memory tool with a self-managed tool interface. Among all systems evaluated, AutoMEM achieves the best cross-scenario generalization capability.

Original Abstract (excerpt)

> LLM 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 ...

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*Automatically collected on 2026-06-05.*