LLMs work best when the user defines their acceptance criteria first

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首先,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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其次,manner, for such Substances as are perceptible by our externall Senses;

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第三,the young King Joash her Son: But Solomon in his own right deposed the

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最后,Gospell; because Moses and the High Priests were the Civill Soveraigns of

另外值得一提的是, ↩︎

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