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为基层减负赋能，强调“要明确权责，不能什么事都压给基层，基层该承担哪些工作，要把职责事项搞清楚”；
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“深化亩均效益改革，让更多低效企业‘减脂瘦身’、优质企业‘强筋壮骨’，激活了工业经济高质量发展‘一池春水’。2025年，全县规模以上工业增加值再创新高，达到86.6亿元，同比增长9.2%。”全椒县工业和信息化局局长池月贵说。。同城约会对此有专业解读
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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