清华团队提出CABLE方法，让AI记忆系统不再只靠相似度，而是通过互补链接找回被遗忘的关键信息。
CABLE是一种插件式内存增强方法，专为解决LLM代理长期记忆中的证据可达性问题。该方法在LoCoMo和MA-LongMemEval基准测试中使用Qwen3.5-27B、DeepSeek-chat和GPT-4o-mini进行评估。CABLE通过构建稀疏的推理相关关联，而非重复主机检索器已覆盖的内容，显著提升了跨记忆和会话的证据检索能力。在开放域、多会话和偏好导向问题类别中，CABLE实现了最大的性能提升。
CABLE: Extending the Reach of Memory Retrieval via Complementary Antecedent-Based Linking and Expansion
As LLM agents operate across structured workflows and sessions, preserving long-term history does not ensure that later contexts can recover relevant evidence through a bounded memory interface. We study this evidence-reachability problem in long-term conversational memory, where retrieval still relies heavily on semantic similarity. This works well for topical recall, but it often misses earlier experiences, plans, or motivations that are semantically distant from the later events they help explain. Existing memory graphs provide cross-memory structure, yet links driven mainly by semantic overlap can duplicate what the host retriever already recovers. We argue that link construction should instead prioritize a sparse set of retriever-complementary associations. We present CABLE (Complementary Antecedent-Based Linking and Expansion), a plug-in augmentation that constructs links designed to extend the host retriever's direct semantic reach. For each new memory, CABLE generates antecedent-oriented queries, retrieves prior memories, subtracts candidates in the direct semantic neighborhood, and verifies the remainder before adding the accepted complementary associations into a sparse directed graph. At retrieval time, CABLE expands the host system's retrieved seeds along these links to surface implicit supporting evidence. We evaluate CABLE with A-MEM on LoCoMo and MA-LongMemEval, and further integrate it into SimpleMem and Mem0g on LoCoMo, using Qwen3.5-27B, DeepSeek-chat, and GPT-4o-mini. CABLE yields higher mean LLM-judge scores in every evaluated system-level setting, with the largest gains in categories where useful evidence is distributed across memories or sessions, including open-domain, multi-session, and preference-oriented questions. These results support prioritizing sparse, reasoning-relevant associations that complement rather than duplicate the host retriever.