Exploring Cross-Scenario Generality of Agentic Memory Systems: Diagnostics and a Strong Baseline

2026-06-02

AI Summary

Current memory systems for large language model agents are predominantly designed for single scenarios, limiting their generalization across diverse tasks. This work systematically evaluates eight memory mechanisms alongside a search-oriented memory framework across five heterogeneous environments to assess their universality. To address these limitations, the paper introduces AutoMEM, a novel mechanism enabling agents to autonomously manage memory storage and retrieval. Departing from conventional fixed-pipeline passive storage, AutoMEM employs flat textual memory representations accessed via tool calls, empowering agents with active control over when and how to store or retrieve memories. Experimental results demonstrate that AutoMEM significantly outperforms baseline approaches in cross-task overall performance, underscoring the critical role of autonomous memory management in enhancing agent generalization.

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. 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.

Problem

• agentic memory
• cross-scenario generality
• memory systems
• LLM agents
• heterogeneous trajectories

Innovation

• agentic memory
• cross-scenario generalization
• self-managed storage
• tool-augmented LLM agents
• memory system evaluation