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InCoder-32B-Thinking: Industrial Code World Model for Thinking
arXiv – CS AI|Jian Yang, Wei Zhang, Jiajun Wu, Junhang Cheng, Tuney Zheng, Fanglin Xu, Weicheng Gu, Lin Jing, Yaxin Du, Joseph Li, Yizhi Li, Yan Xing, Chuan Hao, Ran Tao, Ruihao Gong, Aishan Liu, Zhoujun Li, Mingjie Tang, Chenghua Lin, Siheng Chen, Wayne Xin Zhao, Xianglong Liu, Ming Zhou, Bryan Dai, Weifeng Lv|
🤖AI Summary
Researchers introduce InCoder-32B-Thinking, an AI model trained with Error-driven Chain-of-Thought (ECoT) framework and Industrial Code World Model (ICWM) for industrial software development. The model generates reasoning traces for hardware-constrained programming and achieves top-tier performance on 23 benchmarks, scoring 81.3% on LiveCodeBench v5 and 84.0% on CAD-Coder.
Key Takeaways
- →InCoder-32B-Thinking uses Error-driven Chain-of-Thought synthesis to generate reasoning traces from multi-turn dialogue with environmental feedback.
- →The Industrial Code World Model learns causal dynamics of how code affects hardware behavior through Verilog simulation and GPU profiling data.
- →The model enables self-verification by predicting execution outcomes before actual compilation.
- →Performance results show 81.3% accuracy on LiveCodeBench v5 and 84.0% on CAD-Coder benchmarks.
- →Training data validation through domain toolchains ensures reasoning depth matches real industrial task requirements.
#ai-models#code-generation#hardware-optimization#industrial-software#chain-of-thought#gpu-optimization#verilog#benchmark#reasoning#open-source
Read Original →via arXiv – CS AI
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