in essentially every important file, even those that we might naturally write off by thinking, “obviously someone would have checked that before.”
This round's Champions Cup fixtures present a bleak outlook for traveling sides clinching multiple triumphs. Since the adoption of single-match deciders three seasons prior, hosts have suffered defeat on just two occasions. A substantial shift in this trend would undoubtedly astonish betting agencies.
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Consumers increasingly prefer AI assistants over conventional search. Additionally, integrated AI summaries in search results often eliminate the need for external site visits.
她不想打，但也没办法。客人来问，能不能便宜点？她说不便宜，客人就去别家了。她有时候想，要不算了，反正也饿不死。但第二天起来，又接着发朋友圈，接着直播，接着给老学员发微信问“明天来不来”。
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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)