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LLMs can debug code *without* human-provided test cases, autonomously generating inputs and execution traces to match the performance of public-test-dependent methods while reducing token consumption.
LLMs can generate better code by treating tests as noisy signals to be refined, rather than ground truth, unlocking performance gains even with smaller models.