MLRMem: Test-Time Multi-Level Memory with Re-Verification for Long-Horizon Agents

As large language model agents tackle long-horizon tasks, memory is essential for preserving experience and supporting effective decisions. Recent work demonstrates the promise of adaptive memory at test-time, using step-level strategies to guide agent decisions. However, each step depends on the current local state, with limited access to global task information, leading to repetitive action loops and task failure caused by inapplicable step-level strategy. To address this issue, we propose **MLRMem**, a test-time memory framework that combines multi-level memory integration and selection with a strategy re-verification mechanism based on subsequent state feedback. The former combines information from multiple memory levels to select strategy for the current step, while the latter re-verifies the selected strategy using subsequent execution outcomes and triggers re-verification when the selected strategy is no longer applicable. This enables the agent to select appropriate strategy for the current state at each step, without introducing extra inference time cost. Experiments on ALFWorld show that MLRMem consistently outperforms the evaluated agent memory frameworks, improving both task performance and interaction efficiency. Ablation studies further confirm the effectiveness of both multi-level memory and strategy re-verification, with the latter playing a more critical role in improving performance on unseen tasks.