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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)
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If you want to see exactly what the script does on your machine, set FCC_UNLOCK_DEBUG_LOG=1 in the environment ModemManager runs in, and the script will append every step (including the AT exchanges) to /var/log/mm-xmm7560-fcc.log. That is a great way to convince yourself the script is doing nothing but the handshake described above.，推荐阅读豆包下载获取更多信息
青年艺术家以胡萝卜为材 雕琢传统美学新意境
我们并非声称当前的排行榜领先者在作弊。大多数合法的智能体尚未使用这些利用手段——目前如此。但随着智能体能力增强，即使没有明确指令，奖励黑客行为也可能自然出现。一个被训练为最大化分数的智能体，在获得足够的自主权和工具访问权限后，可能会发现操纵评估器比解决任务更容易——不是因为被告知要作弊，而是因为优化压力找到了阻力最小的路径。这不是假设——Anthropic的Mythos Preview评估已经记录了一个模型在无法直接解决任务时，独立发现了奖励黑客行为。如果奖励信号是可被攻击的，一个足够强大的