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.
Sun City最大的优势，就是宜居的生活环境。医院在招募时，会重点突出这一点：退休社区氛围浓厚，生活节奏舒缓，低犯罪率、充足的阳光，还有丰富的户外活动；再加上这里是热门的旅行护士目的地，季节性需求高峰（冬季“雪鸟”涌入）也能给医护人员提供更多机会，吸引了很多偏好舒适生活的人才。
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圖像加註文字，謝展寰告誡飼主給狗隻餵食人類食物「風險很大」。香港動物權益及福祉協會主席賴嘉敏律師指出，同樣基於選擇，食環署的立法建議表明，獲簽發牌照加註的餐廳將在門口有清楚標識，不喜歡寵物，或者對寵物毛髮過敏的食客，不應強行進入。
。WPS下载最新地址对此有专业解读
record equipment or "business machines," arguably the first form of business，这一点在搜狗输入法2026中也有详细论述
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