daily closing reports and other longer "administrative" output.
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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.。关于这个话题，WPS官方版本下载提供了深入分析
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规模最大，国家水网覆盖范围占国土面积比例达80.3%。“十四五”时期，181项重大水利工程开工建设，省、市、县级水网与国家水网有序衔接，水网“最后一公里”不断打通。，详情可参考搜狗输入法2026
Артем Соколов (Редактор отдела «Силовые структуры»)