CueMem：用于长期对话记忆的线索引导式上下文重建

中文摘要 AI 辅助

长期对话代理必须通过回忆扩展的对话历史来回答用户查询，然而直接使用完整历史成本高昂且往往不可靠，而压缩的记忆单元可能会丢失回答问题所需的细粒度证据。受自传体记忆重建观点的启发，我们提出了CueMem，一个线索引导框架，将提取的记忆记录视为检索线索而非自包含证据，并从其来源轮次重建与查询相关的对话上下文。在记忆构建过程中，CueMem从对话轮次中提取细粒度记忆线索，并将每条线索链接到其来源轮次。在查询时，它检索与查询相关的线索，将其映射到来源轮次锚点，并从一个捕捉时间邻近性和语义相关性的轮次图中从这些锚点扩展，从原始对话中重建一个紧凑的证据上下文，用于LLM答案生成。在LoCoMo和LongMemEval上的实验表明，CueMem始终优于代表性的长期记忆基线。进一步的分析表明，与完整历史LLM设置相比，基于图表的上下文重建有助于恢复支持性对话证据，同时减少查询时输入令牌和延迟。这些结果凸显了检索线索作为长期对话问答中自包含记忆证据的有效替代方案。

英文摘要

Long-term conversational agents must answer user queries by recalling information from extended dialogue histories, yet directly using the full history is costly and often unreliable, while compressed memory units may lose fine-grained evidence needed for question answering. Motivated by the reconstructive view of autobiographical memory, we propose CueMem, a cue-guided framework that treats extracted memory records as retrieval cues rather than self-contained evidence and reconstructs query-relevant dialogue context from their source turns. During memory construction, CueMem extracts fine-grained memory cues from dialogue turns and links each cue to its source turn. At query time, it retrieves query-relevant cues, maps them to source-turn anchors, and expands from these anchors over a turn graph that captures temporal proximity and semantic relatedness, reconstructing a compact evidence context from the original dialogue for LLM answer generation. Experiments on LoCoMo and LongMemEval show that CueMem consistently outperforms representative long-term memory baselines. Further analyses show that graph-based context reconstruction helps recover supporting dialogue evidence while reducing query-time input tokens and latency compared with the full-history LLM setting. These results highlight retrieval cues as an effective alternative to self-contained memory evidence for long-term conversational question answering.

发表机构

• Mashang Consumer Finance Co., Ltd.（马上消费金融股份有限公司）
• Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））

机构由 AI 辅助整理，请以论文原文为准。