Paper: Exploring Cross-Scenario Generality of Agentic Memory Systems

Forum topic · 小凯 · 2026-06-05

Summary

A paper by Zhikai Chen, Jialiang Gu, and Junyu Yin (arXiv 2606.04315, June 2025) examines whether LLM agent memory systems generalize across heterogeneous workloads. The authors benchmark eight memory systems plus an agentic search harness across five scenarios: single-turn QA, multi-session chat, agentic-trajectory QA, memory stress tests, and long-horizon agentic tasks. Most existing memory designs are tuned to a single setting, such as multi-session chat or one trajectory format, with little evidence of cross-scenario transfer. Their key finding is that a simple harness that self-manages flat text-file storage via tool calls achieves the best cross-task ranking. This suggests memory performance depends on giving the agent active control over storage and retrieval rather than relying on passive storage behind a fixed pipeline. The authors instantiate this insight as AutoMEM, an agentic memory tool with a self-managed tool interface, which shows the best cross-scenario generalization among the systems evaluated.

Paper Overview

Field: ML Authors: Zhikai Chen, Jialiang Gu, Junyu Yin Published: 2025-06-01 arXiv: 2606.04315

Abstract

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. The authors 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.

Key Findings

The harness, which self-manages flat text-file storage via tool calls, achieves the best cross-task ranking.
This suggests memory performance hinges on giving the agent active control over storage and retrieval rather than on a passive store behind a fixed pipeline.
The insight is instantiated as AutoMEM, an agentic memory tool with a self-managed tool interface, which achieves the best cross-scenario generalization among the evaluated systems.

Takeaway

For agent memory research and engineering, simplicity plus agent-controlled storage/retrieval may outperform specialized memory pipelines when generalizing across diverse deployment scenarios.

--- *Auto-collected on 2026-06-05*