InCoder-32B-Thinking: Industrial Code World Model for Thinking
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
69d30e112c523d36431facd0
Jian Yang
69d30e112c523d36431facd1
Wei Zhang
claimed_verified
2026-04-10T13:34:30.680Z
69c67b2fa994b07915a6e083
/avatars/3d0fd966df540d34095d2c84ce449180.svg
wei zhang
zwpride
user
zwpride
69d30e112c523d36431facd2
Jiajun Wu
claimed_verified
2026-04-07T13:33:58.297Z
66a8e2538407031e388c501f
/avatars/d16d51f7b1e111efd6d0985995b614be.svg
wjj
wuyuverse
user
wuyuverse
69d30e112c523d36431facd3
Junhang Cheng
claimed_verified
2026-04-07T08:38:51.478Z
67400450e64f7fba857477b5
/avatars/53e780769acd142d72b0578cd6111984.svg
Junhang Cheng
cjhCoder7
user
cjhCoder7
69d30e112c523d36431facd4
Tuney Zheng
69d30e112c523d36431facd5
Fanglin Xu
69d30e112c523d36431facd6
Weicheng Gu
69d30e112c523d36431facd7
Lin Jing
69d30e112c523d36431facd8
Yaxin Du
69d30e112c523d36431facd9
Joseph Li
69d30e112c523d36431facda
Yizhi Li
claimed_verified
2026-04-07T08:38:48.805Z
6382252f54421460665ec501
https://cdn-avatars.huggingface.co/v1/production/uploads/6382252f54421460665ec501/gW9fev3T5QPcNq4f9hqB1.jpeg
Yizhi Li
yizhilll
user
yizhilll
69d30e112c523d36431facdb
Yan Xing
69d30e112c523d36431facdc
Chuan Hao
69d30e112c523d36431facdd
Ran Tao
69d30e112c523d36431facde
Ruihao Gong
69d30e112c523d36431facdf
Aishan Liu
69d30e112c523d36431face0
Zhoujun Li
69d30e112c523d36431face1
Mingjie Tang
69d30e112c523d36431face2
Chenghua Lin
69d30e112c523d36431face3
Siheng Chen
69d30e112c523d36431face4
Wayne Xin Zhao
69d30e112c523d36431face5
Xianglong Liu
69d30e112c523d36431face6
Ming Zhou
69d30e112c523d36431face7
Bryan Dai
69d30e112c523d36431face8
Weifeng Lv
InCoder-32B-Thinking: Industrial Code World Model for Thinking
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
Industrial software development lacks expert reasoning traces for hardware constraints, so a model was trained on error-driven reasoning chains and domain-specific execution traces to generate high-quality code reasoning and performance.
Error-driven Chain-of-Thought industrial code world model reasoning traces Verilog simulation GPU profiling self-verification causal dynamics execution outcomes domain toolchains