MemAgent：学习管理异构记忆提供者以支持LLM智能体
MemAgent: Learning to Manage Heterogeneous Memory Providers for LLM Agents
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中文总结 AI 辅助
MemAgent将智能体记忆建模为路由问题，通过内容感知路由架构和训练数据合成流水线管理异构记忆提供者，在三个基准上平均准确率提升10.0%，优于所有单一记忆方法。
中文摘要 AI 辅助
当前的智能体在跨任务中大多保持无状态，这限制了它们从先前交互中持续改进的能力，使得记忆对于长时程智能体行为至关重要。现有的记忆方法试图复用过去的经验，但大多数依赖于单一的记忆表示（如轨迹、反思、技能、结构化知识），其有效性在不同任务分布中有所差异。重新思考这一设计空间，我们评估了13种记忆方法，发现没有一种方法能在基准测试中普遍适用，这揭示了管理异构记忆提供者的潜力。我们将智能体记忆表述为一个路由问题，其中记忆智能体决定从哪个记忆提供者检索、是否注入短期记忆，以及哪些提供者应存储产生的经验。基于这一视角，我们提出了MemAgent，其特点是具有内容感知的路由架构和训练数据合成流水线。该路由架构在检索前结合内容感知探测，在执行期间进行短期记忆门控，并选择性进行多提供者存储，而训练流水线则为路由决策合成阶段特定的监督。在GAIA、WebWalkerQA和xBench-DS上，MemAgent将平均准确率提高了10.0%，并在所有三个基准测试中优于每一种单独的记忆方法。这些提升伴随着不到0.3%的路由开销和平均任务步骤减少12%。
英文摘要
Current agents remain largely stateless across tasks, limiting their ability to continually improve from prior interactions and making memory essential for long-horizon agentic behavior. Existing memory methods seek to reuse past experience, but most rely on a single memory representation (e.g., trajectories, reflections, skills, structured knowledge) whose effectiveness varies across task distributions. Rethinking this design space, we evaluate 13 memory methods and find that no single method generalizes across benchmarks, revealing the potential of managing heterogeneous memory providers. We formulate agent memory as a routing problem in which a memory agent decides which memory provider to retrieve from, whether to inject short-term memory, and which providers should store the resulting experience. Based on this perspective, we propose MemAgent, featuring a content-aware routing architecture and a training-data synthesis pipeline. The routing architecture combines content-aware probing before retrieval, short-term memory gating during execution, and selective multi-provider storage, while the training pipeline synthesizes phase-specific supervision for routing decisions. Across GAIA, WebWalkerQA, and xBench-DS, MemAgent improves average accuracy by 10.0% and outperforms every individual memory method across all three benchmarks. These gains come with less than 0.3% routing overhead and a 12% reduction in average task steps.
