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Technol."],"published-print":{"date-parts":[[2026,8,31]]},"abstract":"<jats:p>\n                    <jats:bold>Large language models (LLMs)<\/jats:bold>\n                    often suffer from outdated or incorrect knowledge, prompting ongoing research into efficient\n                    <jats:italic toggle=\"yes\">model editing<\/jats:italic>\n                    . Existing methods, however, mainly target individual knowledge facts. When multiple facts need to be edited in a coherent sequence, they frequently lead to deviations or even breakdowns in model\u2019s general abilities. This problem intensifies in\n                    <jats:italic toggle=\"yes\">batch-sequential<\/jats:italic>\n                    editing, where multiple facts are updated simultaneously, compared to\n                    <jats:italic toggle=\"yes\">single-sequential<\/jats:italic>\n                    editing. In this work, by analyzing the parameter matrix, we identify that the degradation stems from unintended modifications that should ideally remain unaffected. These changes accumulate with the number and batch size of edits, ultimately harming editing performance and general abilities. To address this, we propose\n                    <jats:bold>Batch-Aware Editing Anchor Compression (B-EAC)<\/jats:bold>\n                    , a framework tailored for sequential model editing. B-EAC dynamically selects essential anchors for each edit while compressing the influence on nearby parameters. It adopts a layer-wise anchor selection strategy to prevent anchor conflicts during concurrent edits and introduces a rolling anchor refresh mechanism to enhance adaptability across batches. Experiments conducted on three LLMs across four tasks demonstrate that B-EAC effectively suppresses deviation during model editing, achieving a 36.54% performance improvement compared to the case without it. Our work offers a practical and theoretically grounded framework for updating LLMs efficiently, paving the way for continual knowledge refinement in real-world applications.\n                  <\/jats:p>","DOI":"10.1145\/3803803","type":"journal-article","created":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T13:33:26Z","timestamp":1775223206000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Reliable Batch-Sequential Model Editing via Enhanced Editing Anchor Compression"],"prefix":"10.1145","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-4112-184X","authenticated-orcid":false,"given":"Haoxiang","family":"Xu","sequence":"first","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-0123-7023","authenticated-orcid":false,"given":"Ziqi","family":"Peng","sequence":"additional","affiliation":[{"name":"Department of Automation, University of Science and Technology of China, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-6988-4195","authenticated-orcid":false,"given":"Xiaoyu","family":"Wang","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-3018-8178","authenticated-orcid":false,"given":"Hanjie","family":"Guo","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7853-5273","authenticated-orcid":false,"given":"Zhen-Hua","family":"Ling","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,5,18]]},"reference":[{"key":"e_1_3_3_2_2","first-page":"9525","volume-title":"Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018 (NeurIPS \u201918)","author":"Adebayo Julius","year":"2018","unstructured":"Julius Adebayo, Justin Gilmer, Michael Muelly, Ian J. 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