AI RESEARCH
InCoder-32B-Thinking: Industrial Code World Model for Thinking
arXiv CS.AI
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ArXi:2604.03144v1 Announce Type: cross 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.