AI RESEARCH
Exploring Cross-Scenario Generality of Agentic Memory Systems: Diagnostics and a Strong Baseline
arXiv CS.AI
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ArXi:2606.04315v1 Announce Type: new 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.