InCoder-32B-Thinking：面向思維的工業級代碼世界模型

摘要

在晶片設計、GPU優化及嵌入式系統等工業軟體開發領域，現有技術缺乏能展示工程師如何推導硬體約束與時序語義的專家推理軌跡。本研究提出InCoder-32B-Thinking模型，通過基於工業代碼世界模型（ICWM）的錯誤驅動思維鏈（ECoT）合成框架生成推理軌跡。具體而言，ECoT透過融合多輪對話的思考內容與環境錯誤回饋來合成推理鏈，顯式建模錯誤修正過程；ICWM則基於Verilog模擬、GPU性能剖析等領域專屬執行軌跡進行訓練，學習代碼影響硬體行為的因果動態，並能透過預測編譯前執行結果實現自我驗證。所有合成推理軌跡均通過領域工具鏈驗證，形成與工業任務自然推理深度分佈相符的訓練數據。在14個通用基準（LiveCodeBench v5達81.3%）和9個工業基準（CAD-Coder達84.0%，KernelBench達38.0%）的評估中，InCoder-32B-Thinking在所有領域均取得開源模型中最優異的表現。

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-Thinking：面向思維的工業級代碼世界模型

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

摘要

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