RuleMem: Active Rule Memory for Long-Term Conversational Agents
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Computer Science > Computation and Language
arXiv:2609.03915 (cs)
[Submitted on 3 Sep 2026]
Title:RuleMem: Active Rule Memory for Long-Term Conversational Agents
Authors: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).
| Subjects: | Computation and Language (cs.CL); Information Retrieval (cs.IR) |
| Cite as: | arXiv:2609.03915 [cs.CL] |
| (or arXiv:2609.03915v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2609.03915
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled RuleMem: Active Rule Memory for Long-Term Conversational Agents, by Xingyuan Zeng and 7 other authors
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