此次曝光的典型案例中，多个项目存在重大生产安全事故隐患，违规行为触碰安全生产红线，部分项目甚至多项问题叠加，安全风险突出。其中，不乏备受市场关注的住宅项目，如保亿润园，存在多项重大事故隐患：
Мощный удар Израиля по Ирану попал на видео09:41
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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.
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