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X-Intelligence 3.0: Training and Evaluating Reasoning LLM for Semiconductor Display

2025-07-19 · Xiaolin Yan, Yangxing Liu, Jiazhang Zheng, Chi Liu, Mingyu Du, Caisheng Chen, Haoyang Liu, Ming Ding, Yuan Li, Qiuping Liao, Linfeng Li, Zhili Mei, Siyu Wan, Li Li, Ruyi Zhong, Jiangling Yu, Xule Liu, Huihui Hu, Jiameng Yue, Ruohui Cheng, Qi Yang, Liangqing Wu, Ke Zhu, Chi Zhang, Chufei Jing, Yifan Zhou, Yan Liang, Dongdong Li, Zhaohui Wang, Bin Zhao, Mingzhou Wu, Mingzhong Zhou, Peng Du, Zuomin Liao, Chao Dai, Pengfei Liang, Xiaoguang Zhu, Yu Zhang, Yu Gu, Kun Pan, Yuan Wu, Yanqing Guan, Shaojing Wu, Zikang Feng, Xianze Ma, Peishan Cheng, Wenjuan Jiang, Jing Ba, Huihao Yu, Zeping Hu, Yuan Xu, Zhiwei Liu, He Wang, Zhenguo Lin, Ming Liu, Yanhong Meng arxiv

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.

📄 PDF Abstract BibTeX arXiv:2507.14430

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Reinforcement Learning

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