[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-85954-en":3,"doc-seo-85954-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},85954,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Implicit Fine-tuning via Context Engineering: A Curriculum Learning Framework for Multimodal Entity Alignment","Multimodal Entity Alignment (MMEA) seeks equivalent entities across different modalities, while prior context-engineering approaches lack theoretical interpretability and depend heavily on LLM capacity. The work establishes a mathematical equivalence between context engineering and fine-tuning, showing that prompt components emulate contrastive, sequential fine-tuning. It introduces PTFEA, a curriculum-learning-inspired framework with adaptive difficulty modulation using confidence thresholds and three-stage progressive inference from simple to complex cases. Experiments on five public datasets show consistent gains, including a large ICWIKI improvement and major runtime/token reductions versus MM-ChatAlign.","Implicit Fine-tuning via Context Engineering: A Curriculum Learning Framework for Multimodal Entity Alignment  \narXiv :2607 . 10532v 1 [ cs .IR] 12 Jul 2026  \nYunpeng Hong  \n[hongyp@mail.hfut.edu.cn](hongyp@mail.hfut.edu.cn)[ ](hongyp@mail.hfut.edu.cn)Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China), Hefei University of Technology Hefei, Anhui, China  \nChenyang Bu∗ [chenyangbu@hfut.edu.cn](chenyangbu@hfut.edu.cn)[ ](chenyangbu@hfut.edu.cn)Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China), Hefei University of Technology Hefei, Anhui, China  \nDi Wu  \n[wudi.cigit@gmail.com](wudi.cigit@gmail.com)[ ](wudi.cigit@gmail.com)College of Computer and Information Science, Southwest University Chongqing, China  \nYi He  \n[yihe@wm.edu](yihe@wm.edu)  \nDepartment of Data Science, College of William and Mary Williamsburg, VA, USA  \nXindong Wu∗ [xwu@hfut.edu.cn](xwu@hfut.edu.cn)[ ](xwu@hfut.edu.cn)Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China), Hefei University of Technology Hefei, Anhui, China  \nAbstract  \nMultimodal Entity Alignment (MMEA) aims to identify equivalent entities across different modalities. While existing methods enhance MMEA performance through black-box context engineering strategies, their reliance on LLM parameter capacity and lack of theoretical interpretability remain unresolved. To this end, we first theoretically validate the mathematical equivalence between context engineering and model fine-tuning in MMEA tasks, demonstrating that prompt components simulate contrastive learning-based sequential fine-tuning in MMEA. Building on this foundation, we then propose PTFEA, a curriculum-learning-inspired framework that translates fine-tuning strategies into interpretable context engineering. Specifically, adaptive difficulty modulation dynamically adjusts information injection stages using confidence thresholds, establishing mathematical equivalence between curriculum learning weightsand context sample selection; and three-stage progressive inference incorporates entity information from simple to complex cases, mirroring the gradient descent process in fine-tuning. Experiments on five public datasets demonstrate that PTFEA consistently outperforms strong baselines. In particular, on the ICWIKI dataset, PTFEAnarrows the 􀀝 @1 gap between Qwen2.5-72B and 14B to 0.6%. Moreover, compared with the representative context-engineering-based MMEA method MM-ChatAlign, PTFEA reduces the runtime of Qwen2.5-72B from 21 hours to 1 hour and lowers token consumption from 2200–3000 to 200–400, achieving over 80% reduction on the ICWIKI dataset. This work provides the first theoretical framework unifying context engineering and fine-tuning in MMEA, paving the way for future research that seeks to translate additional  \n∗ Corresponding authors.  \nThis work is licensed under a Creative Commons Attribution 4 .0 International License. KDD’26, Jeju Island, Republic of Korea  \n© 2026 Copyright held by the owner/author(s) .  \nACM ISBN 979-8-4007-2259-2/2026/08  \n[https://doi.org/10.1145/3770855.3817732](https://doi.org/10.1145/3770855.3817732)  \nfine-tuning strategies into context engineering paradigms. Our code is available at [https://github.com/DMiC-Lab-HFUT/PTFEA](https://github.com/DMiC-Lab-HFUT/PTFEA).  \nCCS Concepts  \n• Information systems → Deduplication; Information retrieval; • Theory of computation → Data integration.  \nKeywords  \nMultimodal Entity Alignment, Fine-tuning, Curriculum Learning, Context Engineering  \nACM Reference Format:  \nYunpeng Hong, Chenyang Bu, Di Wu, Yi He, and Xindong Wu. 2026. Implicit Fine-tuning via Context Engineering: A Curriculum Learning Framework for Multimodal Entity Alignment. In Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2 (KDD’26), August 09–13, 2026, Jeju Island, Republic of Korea. ACM, New York, NY, USA, 12 pages. 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context engineering via adaptive difficulty modulation using confidence thresholds and a three-stage progressive inference process that moves from simpler to more complex entity 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