Computer Science > Computation and Language
[Submitted on 19 Jul 2025 (v1), last revised 22 Jul 2025 (this version, v2)]
Title:X-Intelligence 3.0: Training and Evaluating Reasoning LLM for Semiconductor Display
View PDF HTML (experimental)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.
Submission history
From: Jiazhang Zheng [view email][v1] Sat, 19 Jul 2025 01:20:39 UTC (1,298 KB)
[v2] Tue, 22 Jul 2025 08:23:15 UTC (1,304 KB)
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