He said he did not blame NASA's contractors for the current slow pace of Artemis launches. Instead, "we should have made better decisions (in the past) and said, you don't go from Artemis II to landing on the moon with Artemis III."
▲生成提示词：一张关于中国传统功夫茶道设计精美的垂直信息图。中国传统水墨画背景。顶部有巨大的、优雅的中文书法标题，明确写着「功夫茶」。向下有三个图文并茂的步骤：步骤 1 展示用沸水温杯，配有中文「温杯」；步骤 2 展示将茶叶放入盖碗，配有中文「投茶」；步骤 3 展示倒出茶汤，配有中文「出汤」。优雅、极简、温暖的大地色调，平衡的布局。
，详情可参考搜狗输入法2026
Waxing Gibbous - More than half is lit up, but it’s not quite full yet.，详情可参考旺商聊官方下载
Author(s): Ramsey Issa, Said Hamad, Ricardo Grau-Crespo, Emad Awad, Taylor D. Sparks。业内人士推荐搜狗输入法2026作为进阶阅读
Even though my dataset is very small, I think it's sufficient to conclude that LLMs can't consistently reason. Also their reasoning performance gets worse as the SAT instance grows, which may be due to the context window becoming too large as the model reasoning progresses, and it gets harder to remember original clauses at the top of the context. A friend of mine made an observation that how complex SAT instances are similar to working with many rules in large codebases. As we add more rules, it gets more and more likely for LLMs to forget some of them, which can be insidious. Of course that doesn't mean LLMs are useless. They can be definitely useful without being able to reason, but due to lack of reasoning, we can't just write down the rules and expect that LLMs will always follow them. For critical requirements there needs to be some other process in place to ensure that these are met.