InCoder-32B-Thinking: Industrial Code World Model for Thinking
Authors: name: Jian Yang, name: Wei Zhang, name: Jiajun Wu, name: Junhang Cheng, name: Tuney Zheng, name: Fanglin Xu, name: Weicheng Gu, name: Lin Jing, name: Yaxin Du, name: Joseph Li, name: Yizhi Li, name: Yan Xing, name: Chuan Hao, name: Ran Tao, name: Ruihao Gong, name: Aishan Liu, name: Zhoujun Li, name: Mingjie Tang, name: Chenghua Lin, name: Siheng Chen, name: Wayne Xin Zhao, name: Xianglong Liu, name: Ming Zhou, name: Bryan Dai, name: Weifeng Lv

Industrial software development across chip design, GPU optimization, and embedded systems lacks expert reasoning traces showing how engineers reason about hardware constraints and timing semantics. In this work, we propose InCoder-32B-Thinking, trained on the data from the Error-driven Chain-of-Thought (ECoT) synthesis framework with an industrial code world model (ICWM) to generate reasoning traces. Specifically, ECoT generates reasoning chains by synthesizing the thinking content from multi-turn dialogue with environmental error feedback, explicitly modeling the error-correction process. ICWM is trained on domain-specific execution traces from Verilog simulation, GPU profiling, etc., learns the causal dynamics of how code affects hardware behavior, and enables self-verification by predicting execution outcomes before actual compilation. All synthesized reasoning traces are validated through domain toolchains, creating training data matching the natural reasoning depth distribution of industrial tasks. Evaluation on 14 general (81.3% on LiveCodeBench v5) and 9 industrial benchmarks (84.0% in CAD-Coder and 38.0% on KernelBench) shows InCoder-32B-Thinking achieves top-tier open-source results across all domains.GPU Optimization