@inproceedings{che-etal-2026-mitigating,
title = "Mitigating Memory Interference in Lifelong Model Editing through Multi-Head Hopfield Retrieval",
author = "Che, Yue and
Guoyi, Chen and
Deng, Xijian and
Wu, Yujie and
Cai, Jiang and
Ran, Xuming and
Xu, Mingkun",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1699/",
pages = "38663--38681",
ISBN = "979-8-89176-459-0",
abstract = "Lifelong model editing seeks to continuously incorporate new knowledge into Large Language Models (LLMs) without costly retraining. Memory-augmented editors preserve pretrained weights to protect unrelated capabilities, but struggle to balance Generalization to rephrased edit queries and Locality on non-edited knowledge. This challenge becomes particularly severe under dense editing settings, where semantically related edits induce substantial memory interference. To tackle this problem, we propose MH-HoReN, a multi-head Hopfield retrieval framework that performs retrieval across complementary subspaces and combines head-wise matching signals for more reliable memory selection. We further extend Hopfield-based editing into a clustered batched scheme that combines shared codebook memories for related edits with token-level pooling for efficient knowledge storage and query routing. Extensive experiments across diverse LLMs and editing benchmarks demonstrate that MH-HoReN achieves better locality-generalization trade-offs as editing scales increase, improving the sum of Locality and Generalization by more than 12{\%} over existing methods while preserving almost perfect Reliability in sequential editing, alongside 7.7{\%} gains in Overall Performance under batched editing. Our code is available here."
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<abstract>Lifelong model editing seeks to continuously incorporate new knowledge into Large Language Models (LLMs) without costly retraining. Memory-augmented editors preserve pretrained weights to protect unrelated capabilities, but struggle to balance Generalization to rephrased edit queries and Locality on non-edited knowledge. This challenge becomes particularly severe under dense editing settings, where semantically related edits induce substantial memory interference. To tackle this problem, we propose MH-HoReN, a multi-head Hopfield retrieval framework that performs retrieval across complementary subspaces and combines head-wise matching signals for more reliable memory selection. We further extend Hopfield-based editing into a clustered batched scheme that combines shared codebook memories for related edits with token-level pooling for efficient knowledge storage and query routing. Extensive experiments across diverse LLMs and editing benchmarks demonstrate that MH-HoReN achieves better locality-generalization trade-offs as editing scales increase, improving the sum of Locality and Generalization by more than 12% over existing methods while preserving almost perfect Reliability in sequential editing, alongside 7.7% gains in Overall Performance under batched editing. Our code is available here.</abstract>
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%0 Conference Proceedings
%T Mitigating Memory Interference in Lifelong Model Editing through Multi-Head Hopfield Retrieval
%A Che, Yue
%A Guoyi, Chen
%A Deng, Xijian
%A Wu, Yujie
%A Cai, Jiang
%A Ran, Xuming
%A Xu, Mingkun
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F che-etal-2026-mitigating
%X Lifelong model editing seeks to continuously incorporate new knowledge into Large Language Models (LLMs) without costly retraining. Memory-augmented editors preserve pretrained weights to protect unrelated capabilities, but struggle to balance Generalization to rephrased edit queries and Locality on non-edited knowledge. This challenge becomes particularly severe under dense editing settings, where semantically related edits induce substantial memory interference. To tackle this problem, we propose MH-HoReN, a multi-head Hopfield retrieval framework that performs retrieval across complementary subspaces and combines head-wise matching signals for more reliable memory selection. We further extend Hopfield-based editing into a clustered batched scheme that combines shared codebook memories for related edits with token-level pooling for efficient knowledge storage and query routing. Extensive experiments across diverse LLMs and editing benchmarks demonstrate that MH-HoReN achieves better locality-generalization trade-offs as editing scales increase, improving the sum of Locality and Generalization by more than 12% over existing methods while preserving almost perfect Reliability in sequential editing, alongside 7.7% gains in Overall Performance under batched editing. Our code is available here.
%U https://aclanthology.org/2026.emnlp-main.1699/
%P 38663-38681
Markdown (Informal)
[Mitigating Memory Interference in Lifelong Model Editing through Multi-Head Hopfield Retrieval](https://aclanthology.org/2026.emnlp-main.1699/) (Che et al., EMNLP 2026)
ACL