InCoder-32B-Thinking: Industrial Code World Model for Thinking
It addresses the problem of missing expert reasoning for engineers in industrial domains like chip design and GPU optimization, though it appears incremental as it builds on existing methods like chain-of-thought.
The paper tackles the lack of expert reasoning traces in industrial software development by proposing InCoder-32B-Thinking, which generates reasoning traces using an industrial code world model and achieves top-tier open-source results, such as 81.3% on LiveCodeBench v5 and 84.0% on CAD-Coder.
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