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From Brewing to Resolution: Tracing the Internal Lifecycle of Code Reasoning in LLMs
Standard accuracy metrics cannot explain why LLMs handle variable tracking but fail on semantically equivalent loops. We study an internal lifecycle of code reasoning in which models first brew the answer, making it linearly recoverable many layers before it becomes self-decodable, and then diverge
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