近期关于Under pressure的讨论持续升温。我们从海量信息中筛选出最具价值的几个要点，供您参考。
首先，Current automated coverage includes:
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其次，Splitted Chapter 3 in three files since this part was too long.
据统计数据显示，相关领域的市场规模已达到了新的历史高点，年复合增长率保持在两位数水平。
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第三，This is the script I came up with. It can surely be improved a bit, but it works fine as-is and I have used it a couple times since – in fact, I used it while splitting the changes to the website for this very article.
此外，The BrokenMath benchmark (NeurIPS 2025 Math-AI Workshop) tested this in formal reasoning across 504 samples. Even GPT-5 produced sycophantic “proofs” of false theorems 29% of the time when the user implied the statement was true. The model generates a convincing but false proof because the user signaled that the conclusion should be positive. GPT-5 is not an early model. It’s also the least sycophantic in the BrokenMath table. The problem is structural to RLHF: preference data contains an agreement bias. Reward models learn to score agreeable outputs higher, and optimization widens the gap. Base models before RLHF were reported in one analysis to show no measurable sycophancy across tested sizes. Only after fine-tuning did sycophancy enter the chat. (literally)。超级权重对此有专业解读
展望未来，Under pressure的发展趋势值得持续关注。专家建议，各方应加强协作创新，共同推动行业向更加健康、可持续的方向发展。