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| Format: | Preprint |
| Published: |
2026
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| Online Access: | https://arxiv.org/abs/2604.08644 |
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| _version_ | 1866915929526370304 |
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| author | Choi, Eunbi Choi, Kibong Chun, Sehyun Hong, Seokhee Hwang, Junwon Jeon, Hyojin Jo, Ahra Jo, Hyunjik Jo, Yeonsik Kim, Joonkee Kim, Seonghwan Kim, Soyeon Kim, Sunkyoung Kim, Yireun Kim, Yongil Lee, Changhun Lee, Haeju Lee, Jinsik Lee, Kyungmin Park, Sangha Ryoo, Kwangrok Seo, Minju Yang, Sejong Yeen, Heuiyeen Chang, Hwan Choi, Stanley Jungkyu Choi, Yejin Han, Kyubeen Jang, Joonwon Jeon, Kijeong Jeong, Geunyeong Jo, Gerrard Jeongwon Jung, Jiyeon Kim, Daeseong Kim, Dohoon Kim, Dohyun Kim, Hyunseo Kim, Minu Kim, Myoungshin Kim, Youchul Ko, Byungoh Lee, Christopher Lee, Edward Hwayoung Lee, Honglak Lee, Jiyoung Lee, Sangeun Lim, Seungwon Lim, Woohyung Mun, Jueun Park, Jaewoo Park, Jimin Park, Jinho Park, Yongmin Seo, Wooseok Song, Yongwoo Yi, Sihyuk Yoo, Kyungjae Yoon, Sangyeon |
| author_facet | Choi, Eunbi Choi, Kibong Chun, Sehyun Hong, Seokhee Hwang, Junwon Jeon, Hyojin Jo, Ahra Jo, Hyunjik Jo, Yeonsik Kim, Joonkee Kim, Seonghwan Kim, Soyeon Kim, Sunkyoung Kim, Yireun Kim, Yongil Lee, Changhun Lee, Haeju Lee, Jinsik Lee, Kyungmin Park, Sangha Ryoo, Kwangrok Seo, Minju Yang, Sejong Yeen, Heuiyeen Chang, Hwan Choi, Stanley Jungkyu Choi, Yejin Han, Kyubeen Jang, Joonwon Jeon, Kijeong Jeong, Geunyeong Jo, Gerrard Jeongwon Jung, Jiyeon Kim, Daeseong Kim, Dohoon Kim, Dohyun Kim, Hyunseo Kim, Minu Kim, Myoungshin Kim, Youchul Ko, Byungoh Lee, Christopher Lee, Edward Hwayoung Lee, Honglak Lee, Jiyoung Lee, Sangeun Lim, Seungwon Lim, Woohyung Mun, Jueun Park, Jaewoo Park, Jimin Park, Jinho Park, Yongmin Seo, Wooseok Song, Yongwoo Yi, Sihyuk Yoo, Kyungjae Yoon, Sangyeon |
| contents | This technical report introduces EXAONE 4.5, the first open-weight vision language model released by LG AI Research. EXAONE 4.5 is architected by integrating a dedicated visual encoder into the existing EXAONE 4.0 framework, enabling native multimodal pretraining over both visual and textual modalities. The model is trained on large-scale data with careful curation, particularly emphasizing document-centric corpora that align with LG's strategic application domains. This targeted data design enables substantial performance gains in document understanding and related tasks, while also delivering broad improvements across general language capabilities. EXAONE 4.5 extends context length up to 256K tokens, facilitating long-context reasoning and enterprise-scale use cases. Comparative evaluations demonstrate that EXAONE 4.5 achieves competitive performance in general benchmarks while outperforming state-of-the-art models of similar scale in document understanding and Korean contextual reasoning. As part of LG's ongoing effort toward practical industrial deployment, EXAONE 4.5 is designed to be continuously extended with additional domains and application scenarios to advance AI for a better life. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_08644 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | EXAONE 4.5 Technical Report Choi, Eunbi Choi, Kibong Chun, Sehyun Hong, Seokhee Hwang, Junwon Jeon, Hyojin Jo, Ahra Jo, Hyunjik Jo, Yeonsik Kim, Joonkee Kim, Seonghwan Kim, Soyeon Kim, Sunkyoung Kim, Yireun Kim, Yongil Lee, Changhun Lee, Haeju Lee, Jinsik Lee, Kyungmin Park, Sangha Ryoo, Kwangrok Seo, Minju Yang, Sejong Yeen, Heuiyeen Chang, Hwan Choi, Stanley Jungkyu Choi, Yejin Han, Kyubeen Jang, Joonwon Jeon, Kijeong Jeong, Geunyeong Jo, Gerrard Jeongwon Jung, Jiyeon Kim, Daeseong Kim, Dohoon Kim, Dohyun Kim, Hyunseo Kim, Minu Kim, Myoungshin Kim, Youchul Ko, Byungoh Lee, Christopher Lee, Edward Hwayoung Lee, Honglak Lee, Jiyoung Lee, Sangeun Lim, Seungwon Lim, Woohyung Mun, Jueun Park, Jaewoo Park, Jimin Park, Jinho Park, Yongmin Seo, Wooseok Song, Yongwoo Yi, Sihyuk Yoo, Kyungjae Yoon, Sangyeon Computation and Language This technical report introduces EXAONE 4.5, the first open-weight vision language model released by LG AI Research. EXAONE 4.5 is architected by integrating a dedicated visual encoder into the existing EXAONE 4.0 framework, enabling native multimodal pretraining over both visual and textual modalities. The model is trained on large-scale data with careful curation, particularly emphasizing document-centric corpora that align with LG's strategic application domains. This targeted data design enables substantial performance gains in document understanding and related tasks, while also delivering broad improvements across general language capabilities. EXAONE 4.5 extends context length up to 256K tokens, facilitating long-context reasoning and enterprise-scale use cases. Comparative evaluations demonstrate that EXAONE 4.5 achieves competitive performance in general benchmarks while outperforming state-of-the-art models of similar scale in document understanding and Korean contextual reasoning. As part of LG's ongoing effort toward practical industrial deployment, EXAONE 4.5 is designed to be continuously extended with additional domains and application scenarios to advance AI for a better life. |
| title | EXAONE 4.5 Technical Report |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2604.08644 |