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实验证明，即便是完全不同时期的社会新闻和话题，大语言模型还是能见微知著，从写作风格、兴趣甚至是人口统计特征，从包含数千名干扰者的候选池中准确找到用户“过去的自己”。
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This started with Addition Under Pressure, where I gave Claude Code and Codex the same prompt: train the smallest possible transformer that can do 10-digit addition with at least 99% accuracy. Claude Code came back with 6,080 parameters and Codex came back with 1,644. The community has since pushed this dramatically lower.