After OpenAI released GPT-5.3-Codex (high) which performed substantially better and faster at these types of tasks than GPT-5.2-Codex, I asked Codex to write a UMAP implementation from scratch in Rust, which at a glance seemed to work and gave reasonable results. I also instructed it to create benchmarks that test a wide variety of representative input matrix sizes. Rust has a popular benchmarking crate in criterion, which outputs the benchmark results in an easy-to-read format, which, most importantly, agents can easily parse.
首先，大模型本身没那么可靠：存在无法根除的幻觉问题、知识时效性问题，任务拆解和规划经常不合理，也缺乏面向特定任务的系统性校验机制。这样一来，以其为“大脑”的智能体使用价值会大打折扣：智能体把模型从“对话”推向“行动”，错误不再只是答错问题，而是可能引发实际操作风险；而真实业务任务往往是跨系统、长链路的，一次小错误会在链路中层层放大，令长链路任务的失败率居高不下（例如单步成功率为95%时，一个 20步链路的整体成功率只有约 36%）。
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Number (12): Everything in this space must add up to 12. The answer is 2-6, placed vertically; 6-1, placed vertically.