@inproceedings{yu-etal-2026-ladderedit,
title = "{L}adder{E}dit: Edit-Level Residual Compression for Memory-Efficient Lifelong Editing of {LLM}s",
author = "Yu, Xiaobing and
Qiu, Peijie and
Yang, Jin and
Dong, Xuanzhao and
Ma, Weiwei and
An, Zhaoqi and
Zhao, Xiaoqi and
Liu, Xiaofeng",
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.1261/",
pages = "28738--28764",
ISBN = "979-8-89176-459-0",
abstract = "Lifelong editing of LLMs requires storing thousands of edits after acquisition. A widely used family of approaches attaches one LoRA adapter per edit, which preserves behavior but grows linearly in storage. To address this challenge, we propose LadderEdit, a method that compresses each LoRA adapter after it is acquired. Each edit is first stored at low rank as a cheap sketch. We then check whether this sketch still satisfies the rewrite, generalization, and locality contract on probe prompts. Edits that pass keep the sketch; those that fail are promoted to a higher rank along a ladder until the contract is met. Because every edit retains some representation, coverage is maintained, and only hard edits consume more rank. Across ZsRE, CounterFact, and WikiBigEdit benchmarks on LLaMA-3-8B, Mistral-7B, and Qwen2.5-7B, LadderEdit tracks exact LoRA storage at $5.2\times$ less memory and remains effective at $50{,}000$ sequential edits."
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<abstract>Lifelong editing of LLMs requires storing thousands of edits after acquisition. A widely used family of approaches attaches one LoRA adapter per edit, which preserves behavior but grows linearly in storage. To address this challenge, we propose LadderEdit, a method that compresses each LoRA adapter after it is acquired. Each edit is first stored at low rank as a cheap sketch. We then check whether this sketch still satisfies the rewrite, generalization, and locality contract on probe prompts. Edits that pass keep the sketch; those that fail are promoted to a higher rank along a ladder until the contract is met. Because every edit retains some representation, coverage is maintained, and only hard edits consume more rank. Across ZsRE, CounterFact, and WikiBigEdit benchmarks on LLaMA-3-8B, Mistral-7B, and Qwen2.5-7B, LadderEdit tracks exact LoRA storage at 5.2\times less memory and remains effective at 50,000 sequential edits.</abstract>
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%0 Conference Proceedings
%T LadderEdit: Edit-Level Residual Compression for Memory-Efficient Lifelong Editing of LLMs
%A Yu, Xiaobing
%A Qiu, Peijie
%A Yang, Jin
%A Dong, Xuanzhao
%A Ma, Weiwei
%A An, Zhaoqi
%A Zhao, Xiaoqi
%A Liu, Xiaofeng
%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 yu-etal-2026-ladderedit
%X Lifelong editing of LLMs requires storing thousands of edits after acquisition. A widely used family of approaches attaches one LoRA adapter per edit, which preserves behavior but grows linearly in storage. To address this challenge, we propose LadderEdit, a method that compresses each LoRA adapter after it is acquired. Each edit is first stored at low rank as a cheap sketch. We then check whether this sketch still satisfies the rewrite, generalization, and locality contract on probe prompts. Edits that pass keep the sketch; those that fail are promoted to a higher rank along a ladder until the contract is met. Because every edit retains some representation, coverage is maintained, and only hard edits consume more rank. Across ZsRE, CounterFact, and WikiBigEdit benchmarks on LLaMA-3-8B, Mistral-7B, and Qwen2.5-7B, LadderEdit tracks exact LoRA storage at 5.2\times less memory and remains effective at 50,000 sequential edits.
%U https://aclanthology.org/2026.emnlp-main.1261/
%P 28738-28764
Markdown (Informal)
[LadderEdit: Edit-Level Residual Compression for Memory-Efficient Lifelong Editing of LLMs](https://aclanthology.org/2026.emnlp-main.1261/) (Yu et al., EMNLP 2026)
ACL
- Xiaobing Yu, Peijie Qiu, Jin Yang, Xuanzhao Dong, Weiwei Ma, Zhaoqi An, Xiaoqi Zhao, and Xiaofeng Liu. 2026. LadderEdit: Edit-Level Residual Compression for Memory-Efficient Lifelong Editing of LLMs. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 28738–28764, Budapest, Hungary. Association for Computational Linguistics.