（一）5000总吨以下的船舶，赔偿责任限额为4510000计算单位；
2025年，云南省昭通市地区生产总值突破2100亿元，经济总量跃居云南省第五，成功迈入全省发展“第一方阵”，走出了一条以产兴城、以城聚人、以人兴业的产城人融合发展新路子，中国式现代化昭通实践迈出了坚实步伐。
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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.