InCoder-32B-Thinking: Industrial Code World Model for Thinking
Abstrak
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
Penulis (25)
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
Akses Cepat
- Tahun Terbit
- 2026
- Bahasa
- en
- Sumber Database
- arXiv
- Akses
- Open Access ✓