{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T17:59:37Z","timestamp":1772042377584,"version":"3.50.1"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686318","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:00:00Z","timestamp":1761004800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,21]]},"abstract":"<jats:p>Assembly code analysis and comprehension play critical roles in applications like reverse engineering, yet they face substantial challenges due to low information density and a lack of explicit syntactic structures. While traditional masked language modeling (MLM) approaches do not explicitly focus on natural language interaction, emerging decoder-focused large language models (LLMs) demonstrate partial success in binary analysis yet remain underexplored for holistic comprehension. We present Assembly Augmented Tuning (ASMA-Tune), an end-to-end structural-semantic instruction tuning framework that synergizes encoder architecture with decoder-based LLMs through a projector module, where the assembly encoder extracts hardware-level structural features, the projector bridges representations with the semantic space, and the instruction-tuned LLM preserves natural language capabilities. Experimental results demonstrate three key advantages: (1) State-of-the-art performance in assembly comprehension with +39.7% Recall@1 and +17.8% MRR improvements over GPT-4-Turbo, (2) Consistent enhancements across base models (24.6\u2013107.4% Recall@1 and 15.2\u2013106.3% MRR on Qwen2.5-Coder, Deepseek-Coder and CodeLlama variants), and (3) Superior instruction-following capabilities (41.5%\u2013118% improvements) with controlled code generation degradation (\u20138.9% to \u201335% across architectures).<\/jats:p>","DOI":"10.3233\/faia251283","type":"book-chapter","created":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:56:58Z","timestamp":1761127018000},"source":"Crossref","is-referenced-by-count":1,"title":["ASMA-Tune: Unlocking LLMs\u2019 Assembly Code Comprehension via Structural-Semantic Instruction Tuning"],"prefix":"10.3233","author":[{"given":"Xinyi","family":"Wang","sequence":"first","affiliation":[{"name":"Nankai University (College of Cryptology and Cyber Science, Nankai University), TKLNDST (Tianjin Key Laboratory of Network and Data Security Technology) & DISSEC (Key Laboratory of Data and Intelligent System Security, Ministry of Education, China), Tianjin, China"}]},{"given":"Jiashui","family":"Wang","sequence":"additional","affiliation":[{"name":"Ant Group"},{"name":"Zhejiang University"}]},{"given":"Jinbo","family":"Su","sequence":"additional","affiliation":[{"name":"Renmin University of China"}]},{"given":"Ke","family":"Wang","sequence":"additional","affiliation":[{"name":"Renmin University of China"}]},{"given":"Peng","family":"Chen","sequence":"additional","affiliation":[{"name":"University of the Chinese Academy of Sciences"}]},{"given":"Yanming","family":"Liu","sequence":"additional","affiliation":[{"name":"Zhejiang University"}]},{"given":"Long","family":"Liu","sequence":"additional","affiliation":[{"name":"Ant Group"}]},{"given":"Xiang","family":"Li","sequence":"additional","affiliation":[{"name":"Nankai University (College of Cryptology and Cyber Science, Nankai University), TKLNDST (Tianjin Key Laboratory of Network and Data Security Technology) & DISSEC (Key Laboratory of Data and Intelligent System Security, Ministry of Education, China), Tianjin, China"}]},{"given":"Yangdong","family":"Wang","sequence":"additional","affiliation":[{"name":"Ant Group"}]},{"given":"Qiyuan","family":"Chen","sequence":"additional","affiliation":[{"name":"Ant Group"}]},{"given":"Rongze","family":"Chen","sequence":"additional","affiliation":[{"name":"Nankai University (College of Cryptology and Cyber Science, Nankai University), TKLNDST (Tianjin Key Laboratory of Network and Data Security Technology) & DISSEC (Key Laboratory of Data and Intelligent System Security, Ministry of Education, China), Tianjin, China"}]},{"given":"Chunfu","family":"Jia","sequence":"additional","affiliation":[{"name":"Nankai University (College of Cryptology and Cyber Science, Nankai University), TKLNDST (Tianjin Key Laboratory of Network and Data Security Technology) & DISSEC (Key Laboratory of Data and Intelligent System Security, Ministry of Education, China), Tianjin, China"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2025"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA251283","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:56:59Z","timestamp":1761127019000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA251283"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,21]]},"ISBN":["9781643686318"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia251283","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,21]]}}}