mlsanityData to 14 Oct 2026

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

arXiv 2507.14430 ↗First posted 19 Jul 2025cs.CLcited by 0

On the map: LLM reasoning → Reasoning benchmarks and evaluation

Abstract (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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning. Builds on 12 of its 32 references that are in the map. First posted on arXiv 19 Jul 2025.

Builds on

12 of 32 references.

Closest ancestors · 5

Same direction, newest first

  1. 2025Qwen3 Technical Report14 May
  2. 2025DeepDistill: Enhancing LLM Reasoning Capabilities via Large-Scale Difficulty-Graded Data Training24 Apr
  3. 2025Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language Models16 Jan
  4. 2024DeepSeek-V3 Technical Report27 Dec
  5. 2024Multi-Step Reasoning with Large Language Models, a Survey16 Jul

Other references · 7

  1. 2025DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learningcited by 6,330
  2. 2024M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillationcited by 1,073
  3. 2023Ragas: Automated Evaluation of Retrieval Augmented Generationcited by 418
  4. 2024A Survey on Retrieval-Augmented Text Generation for Large Language Modelscited by 77
  5. 2025Scaling Laws of Synthetic Data for Language Modelscited by 46
  6. 2024Conan-embedding: General Text Embedding with More and Better Negative Samplescited by 17
  7. 2025A Framework for Situating Innovations, Opportunities, and Challenges in Advancing Vertical Systems with Large AI Modelscited by 2

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  1. 2026X-RAY: Mapping LLM Reasoning Capability via Formalized and Calibrated Probes
  2. 2024Beyond LLMs: Advancing the Landscape of Complex Reasoning
  3. 2025Evaluating Mathematical Reasoning Across Large Language Models: A Fine-Grained Approach
  4. 2025Xmodel-2.5: 1.3B Data-Efficient Reasoning SLM
  5. 2023X-InstructBLIP: A Framework for aligning X-Modal instruction-aware representations to LLMs and Emergent Cross-modal Reasoning
  6. 2025EEsizer: LLM-Based AI Agent for Sizing of Analog and Mixed Signal Circuit
  7. 2025LLM-Cave: A benchmark and light environment for large language models reasoning and decision-making system
  8. 2025An Analysis of Architectural Impact on LLM-based Abstract Visual Reasoning: A Systematic Benchmark on RAVEN-FAIR