X-Intelligence 3.0: Training and Evaluating Reasoning LLM for Semiconductor Display
Abstract
Large language models (LLMs) have recently achieved significant advances in reasoning and demonstrated their advantages in solving challenging problems. Yet, their effectiveness in the semiconductor display industry remains limited due to a lack of domain-specific training and expertise. To bridge this gap, we present X-Intelligence 3.0, the first high-performance reasoning model specifically developed for the semiconductor display industry. This model is designed to deliver expert-level understanding and reasoning for the industry's complex challenges. Leveraging a carefully curated industry knowledge base, the model undergoes supervised fine-tuning and reinforcement learning to enhance its reasoning and comprehension capabilities. To further accelerate development, we implemented an automated evaluation framework that simulates expert-level assessments. We also integrated a domain-specific retrieval-augmented generation (RAG) mechanism, resulting in notable performance gains on benchmark datasets. Despite its relatively compact size of 32 billion parameters, X-Intelligence 3.0 outperforms SOTA DeepSeek-R1-671B across multiple evaluations. This demonstrates its exceptional efficiency and establishes it as a powerful solution to the longstanding reasoning challenges faced by the semiconductor display industry.
Cite
@article{arxiv.2507.14430,
title = {X-Intelligence 3.0: Training and Evaluating Reasoning LLM for Semiconductor Display},
author = {Xiaolin Yan and Yangxing Liu and Jiazhang Zheng and Chi Liu and Mingyu Du and Caisheng Chen and Haoyang Liu and Ming Ding and Yuan Li and Qiuping Liao and Linfeng Li and Zhili Mei and Siyu Wan and Li Li and Ruyi Zhong and Jiangling Yu and Xule Liu and Huihui Hu and Jiameng Yue and Ruohui Cheng and Qi Yang and Liangqing Wu and Ke Zhu and Chi Zhang and Chufei Jing and Yifan Zhou and Yan Liang and Dongdong Li and Zhaohui Wang and Bin Zhao and Mingzhou Wu and Mingzhong Zhou and Peng Du and Zuomin Liao and Chao Dai and Pengfei Liang and Xiaoguang Zhu and Yu Zhang and Yu Gu and Kun Pan and Yuan Wu and Yanqing Guan and Shaojing Wu and Zikang Feng and Xianze Ma and Peishan Cheng and Wenjuan Jiang and Jing Ba and Huihao Yu and Zeping Hu and Yuan Xu and Zhiwei Liu and He Wang and Zhenguo Lin and Ming Liu and Yanhong Meng},
journal= {arXiv preprint arXiv:2507.14430},
year = {2025}
}
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