RuleMem：面向长期对话智能体的主动规则记忆

中文摘要 AI 辅助

长期对话中的问答智能体需对海量、时间分散的对话历史进行推理，但现有记忆机制主要将过往信息视为被动存储的事实，导致语义鸿沟与不可靠推理。为解决这一局限，我们提出RuleMem，一种基于规则的记忆框架，可从历史交互中归纳可复用的逻辑规则，主动指导证据检索与推理。具体而言，RuleMem从对话中构建自然语言Horn子句，并通过规则困惑度一致性（RPC）机制对其进行验证。这些归纳出的规则可检索语义距离较远的证据，同时为答案生成提供显式逻辑结构。我们在两个长期对话基准LoCoMo和LongMemEval_s*上对RuleMem进行了全面评估，在LoCoMo上与14个基线模型的严格对比中，RuleMem取得了最高准确率，超出基线平均值27.47个百分点（相对提升54.3%）。

英文摘要

Question answering agents in long-term conversations must reason over massive, temporally dispersed dialogue histories. However, existing memory mechanisms primarily treat past information as \textit{passively} stored facts, leading to semantic gaps and unreliable reasoning. To address this limitation, we propose RuleMem, a rule-based memory framework that induces reusable logical rules from historical interactions to \textit{actively} guide both evidence retrieval and reasoning. Specifically, RuleMem constructs natural-language Horn clauses from conversations and validates them via a Rule Perplexity Consistency (RPC) mechanism. These induced rules enable the retrieval of semantically distant evidence while providing an explicit logical structure for answer generation. We conducted a comprehensive evaluation of RuleMem on two long-term conversational benchmarks, LoCoMo and LongMemEval_s*. In a rigorous comparison against 14 baselines on LoCoMo, RuleMem achieved the highest accuracy, exceeding the baseline average by 27.47 points (a 54.3% relative improvement).

发表机构

• Sun Yat-sen University（中山大学）
• The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
• Shenzhen Institute of Computing Sciences（深圳计算科学研究院）
• The Hong Kong Polytechnic University（香港理工大学）
• Tsinghua University（清华大学）

机构由 AI 辅助整理，请以论文原文为准。