Representation Interventions Enable Lifelong Knowledge Memory Control in LLMs

Publication Date: 7/8/2026

Event: The 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026)

Reference: pp. 5414–5436, 2026

Authors: Xuyuan Liu, Dartmouth College; NEC Laboratories America, Inc.; Zhengzhang Chen, NEC Laboratories America, Inc.; Xinshuai Dong, Carnegie Mellon University; NEC Laboratories America, Inc.; Yanchi Liu, NEC Laboratories America, Inc.; Xujiang Zhao, NEC Laboratories America, Inc.; Shengyu Chen, NEC Laboratories America, Inc.; Haoyu Wang, NEC Laboratories America, Inc.; Yujun Yan, Dartmouth College; Haifeng Chen, NEC Laboratories America, Inc.

Abstract: Large language models (LLMs) often produce incorrect or outdated content after being employed. Efficient and accurate knowledge up-dates without costly retraining are a major challenge. This problem is particularly challenging in lifelong settings, where complex, unstructured knowledge must coexist without interference. We introduce RILKE (Representation Intervention for Lifelong KnowledgE Control), a robust and scalable method that treats knowledge control as interventions within the model’s representation space. Leveraging representation-space expressiveness, we identify two key properties enabling RILKE to achieve fine-grained control over complex, un-structured knowledge while maintaining general utility with frozen base weights. During training, RILKE learns paraphrase-robust and edit-localized modules that limit each update to a low-dimensional subspace to minimize cross-edit interference. At inference, a query-adaptive router selects the appropriate module to guide the model’s generation. Across LLaMA and Qwen models, RILKE scales effectively to large-scale benchmarks, demonstrating high edit success and strong paraphrase generalization while preserving general utility with modest memory overhead. These results show RILKE is an effective and scalable solution for lifelong knowledge control in LLMs.

Publication Link: https://aclanthology.org/2026.acl-long.246/