3. 5#楼天井操作平台未按要求编制审批危大方案，且连墙件间距偏大、无剪刀撑；5#楼爬架高度14米，局部两道支座， 顶部悬高超6米无临时拉结措施；且爬架与结构外墙间隙大于15cm，四周全高范围上下贯通无中部翻板。（违反《房屋市政工程生产安全重大事故隐患判定标准（2024版）》第四条第四款、第九条第四款，属于重大事故隐患。）
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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下载最新地址提供了深入分析
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