Long-term memory is essential for conversational agents to remain coherent across extended dialogues, follow through on commitments made many sessions earlier, and adapt their behaviour to each user. Current LLM-backed long-term conversational memory, however, is reachability-bounded by the similarity between a query and stored content, both lexical and dense-vector. The approach is effective when query and memory share surface features such as wording or named entities (we call this descriptive). But it misses another, equally valuable class of cases, where query and memory do not share surface features and are tied only by a latent semantic arc (associative). On this regime prevailing long-term memory systems collectively fail. Covering this other half is what allows an assistant, for the first time, to actively draw on past dialogue as a semantic asset. On the memory side, this is the engineering counterpart of what cognitive science calls episodic future thinking: rehearsing past experience for the future contexts under which it will need to be found. We call these write-time rehearsals triggers. We propose T-Mem, the first long-term conversational memory architecture that covers both descriptive and associative recall. At each of two evidence granularities, single facts and full exchanges, T-Mem instantiates one descriptive trigger family and one associative trigger family, so that every memory remains reachable from both surface-similar and relevance-bound queries. As empirical validation, T-Mem reaches state-of-the-art on both LoCoMo and LoCoMo-Plus.

翻译：长期记忆是对话智能体在长程对话中保持连贯性、跨会话执行先前承诺并调整行为以适应不同用户的关键能力。然而，当前基于大语言模型的长期对话记忆系统受限于查询与存储内容之间（包括词法与稠密向量）的相似性。当查询与记忆共享字面特征（如措辞或命名实体，我们称之为描述性匹配）时，该方法效果显著，但忽略了另一类同样重要的情形：查询与记忆虽无表面特征关联，却存在潜在语义弧（联想性匹配）。现有主流长期记忆系统在此类场景中集体失效。覆盖这一缺失领域，将使智能助手首次能够主动将过往对话作为语义资产加以运用。从记忆构建角度而言，这对应认知科学中"情景预期记忆"的工程实现：预演过往经验以便未来情境中有效检索。我们将此类写入时的预演机制称为触发式线索。本文提出T-Mem，首个同时覆盖描述性与联想性回忆的长期对话记忆架构。在单事实与完整对话两种证据粒度上，T-Mem分别部署一类描述性触发线索与一类联想性触发线索，确保每条记忆均可通过表面相似查询与相关性约束查询双重路径定位。实验验证表明，T-Mem在LoCoMo与LoCoMo-Plus基准上均达到最优性能。