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Discarding irrelevant reasoning tokens can make language models up to 3x faster at test time without sacrificing performance.
Native computer use can be achieved at scale, enabling agents to outperform leading systems while significantly enhancing security against adversarial attacks.
LLMs can solve competitive coding problems much more reliably by actively searching for the *right* test cases, rather than relying on random or pre-defined inputs.
