Zero-Mem：面向LLM智能体的零Token记忆操作

摘要

LLM智能体需要记忆才能在长时间交互中保持一致的行为，但许多系统依赖额外的LLM调用来操作这些记忆。生成中间记录并协调其检索会增加持续的token和时间成本，而被省略或合并的细节可能掩盖原始证据。我们探究的问题是：结构化记忆访问是否根本需要生成过程。Zero-Mem引入了零token记忆操作：除最终问答之外的任何步骤都不会调用LLM，也不消耗LLM的输入或输出token；编码器计算被单独核算。Zero-Mem保留原始交互轨迹作为其记录来源，并以两种互补的方式组织这些轨迹。实体-上下文图揭示交互之间的连接，而时间层次结构保留对话的局部性和会话状态。对于每个查询，Zero-Mem权衡两种视图，从两者中检索，并沿着其结构恢复支持性关系或周围上下文。确定性校准首先丢弃矛盾证据，然后将阅读器的答案锚定在检索到的轨迹中。只有最终问答阅读器才会调用LLM。在长记忆和长上下文问答基准测试中，Zero-Mem在消除记忆操作中的LLM调用和LLM token消耗的同时，达到了具有竞争力的性能。在相同的最终问答阅读器和上下文预算下，与最快的对比基线相比，Zero-Mem将记忆操作的时间成本降低了57.6%。消融实验验证了两种视图及其查询依赖协调的贡献。总体而言，结果表明结构化智能体记忆无需生成过去的中间表示。经过同行评审后，代码和实现细节将在https://github.com/TheMoon0815/Zero-mem上提供。

English

LLM agents need memory to act consistently over long interactions, yet many systems use additional LLM calls to operate that memory. Generating intermediate records and mediating their retrieval adds recurring token and time costs, while omitted or merged details can obscure the original evidence. We ask whether structured memory access requires generation at all. Zero-Mem introduces zero-token memory operations: no step outside final question answering invokes an LLM or consumes LLM input or output tokens; encoder computation is accounted for separately. Zero-Mem preserves original interaction traces as its source of record. It organizes the traces in two complementary ways. An entity--context graph exposes connections across interactions, while a temporal hierarchy preserves conversational locality and session state. For each query, Zero-Mem weighs the two views, retrieves from both, and follows their structure to recover supporting relations or surrounding context. Deterministic calibration first discards conflicting evidence and then keeps the reader's answer grounded in the retrieved traces. Only the final-QA reader invokes an LLM. Across long-memory and long-context question-answering benchmarks, Zero-Mem achieves competitive performance while eliminating LLM calls and LLM-token consumption from memory operations. With the same final-QA reader and context budget, it reduces memory-operation time cost by 57.6\% relative to the fastest compared baseline. Ablations support the contribution of the two views and their query-dependent coordination. Overall, the results show that structured agent memory need not generate an intermediate representation of the past. After peer review, the code and implementation details will be available at blue{https://github.com/TheMoon0815/Zero-mem}.