Exploring Cross-Scenario Generality of Agentic Memory Systems: Diagnostics and a Strong Baseline
arXiv:2606.04315v1 Announce Type: new 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 tas
The rapid development and deployment of LLM agents are creating immediate challenges in managing their context windows and memory, necessitating robust solutions to improve their practicality and generality.
Improving the generality and reliability of agentic memory systems is crucial for the widespread adoption and effectiveness of autonomous AI agents across diverse real-world applications.
The focus is shifting from scenario-specific memory designs to foundational systems that can generalize across varied agentic tasks and trajectories, enabling more robust and versatile AI agents.
- · AI research labs
- · AI developers
- · Enterprises deploying AI agents
- · Developers of custom, non-generalizable memory systems
More sophisticated and reliable AI agents become feasible for complex, multi-stage tasks.
Increased trust and integration of AI agents into critical workflows, potentially displacing traditional software and human tasks.
Accelerated development of fully autonomous systems capable of long-horizon problem-solving with minimal human oversight.
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Read at arXiv cs.AI