做 LLM 智能体长期记忆系统的团队会发现，现有方案在依赖推理上几乎失效——MEME 基准暴露了被忽视的盲区，值得点开看看你的系统能否通过级联和缺失任务。
MEME 是一个针对 LLM 智能体在多会话环境中记忆能力的新基准，覆盖了多实体和演化两个维度的六项任务，其中三项（级联、缺失、删除）是此前工作未评估的。在 100 个受控场景中测试了六种记忆系统，发现所有系统在默认配置下的依赖推理任务上表现极差（级联任务平均准确率 3%，缺失任务 1%），尽管静态检索性能尚可。提示优化、更深层检索、减少填充噪声以及更强的大模型都无法弥补这一差距。只有基于文件的智能体配合 Claude Opus 4.7 能部分改善，但成本是基准方案的约 70 倍，说明当前依赖推理的解决方案不具备可扩展性。
MEME: Multi-entity & Evolving Memory Evaluation
LLM-based agents increasingly operate in persistent environments where they must store, update, and reason over information across many sessions. While prior benchmarks evaluate only single-entity updates, MEME defines six tasks spanning the full space defined by the multi-entity and evolving axes, including three not scored by prior work: Cascade and Absence (dependency reasoning) and Deletion (post-removal state). Evaluating six memory systems spanning three memory paradigms on 100 controlled episodes, we find that all systems collapse on dependency reasoning under the default configuration (Cascade: 3%, Absence: 1% in average accuracy) despite adequate static retrieval performance. Prompt optimization, deeper retrieval, reduced filler noise, and most stronger LLMs fail to close this gap. Only a file-based agent paired with Claude Opus 4.7 as its internal LLM partially closes the gap, but at ~70x the baseline cost, indicating closure currently depends on configurations that are not practical at scale. Code and data are available on the project page: https://seokwonjung-jay.github.io/meme-eval/.