InCoder-32B-思维：面向思维过程的工业代码世界模型

摘要

在芯片设计、GPU优化及嵌入式系统等工业软件开发领域，当前缺乏能够展现工程师如何推演硬件约束与时序语义的专家级推理轨迹。本研究提出InCoder-32B-Thinking模型，该模型基于工业代码世界模型（ICWM）驱动的错误链式思维（ECoT）合成框架生成的数据进行训练，专门用于生成推理轨迹。具体而言，ECoT通过融合多轮对话中的思维内容与环境错误反馈来合成推理链，显式建模了错误修正过程。ICWM则基于Verilog仿真、GPU性能分析等特定领域的执行轨迹进行训练，学习代码如何影响硬件行为的因果动态，并通过在实际编译前预测执行结果实现自我验证。所有合成的推理轨迹均通过领域工具链验证，形成的训练数据与工业任务的自然推理深度分布相匹配。在14个通用基准（LiveCodeBench v5达81.3%）和9个工业基准（CAD-Coder达84.0%，KernelBench达38.0%）上的评估表明，InCoder-32B-Thinking在所有领域均取得开源模型的顶尖结果。

English

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

InCoder-32B-思维：面向思维过程的工业代码世界模型

InCoder-32B-Thinking: Industrial Code World Model for Thinking

摘要

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