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Coding agent benchmarks can now dynamically scale difficulty and avoid data leakage by having agents evaluate each other, with the latest models generating tasks that defeat older solvers 10x more often.
In-code comments only help LLMs when they smuggle in correct downstream solution content that the model could not otherwise elicit, debunking the idea that self-commentary acts as general chain-of-thought scaffolding.
