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arXiv · 2609.03915

RuleMem: Active Rule Memory for Long-Term Conversational Agents

Xingyuan Zeng·Zuohan Wu·Quanming Yao·Yue Wang·Wei Liu·Libin Zheng·Jiuke Wang·Jian Yin
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Abstract

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).

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Xingyuan Zeng, Zuohan Wu, Quanming Yao, Yue Wang, Wei Liu, Libin Zheng, Jiuke Wang, Jian Yin. 2026-09-03. RuleMem: Active Rule Memory for Long-Term Conversational Agents. https://arxiv.org/abs/2609.03915

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