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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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2021—2025 年度，44 个行业研发投入金额呈现整体扩张的态势，说明重视研发不只是龙头和腰部企业的行为，而且成为了更加广泛的共同行动。另一方面，研发均值下滑的行业集中在末端，印证了行业整体分化的现象。典型案例来自房地产和教育行业，在“去杠杆”“双减”等政策催化下，市场步入格局重塑阶段，企业纷纷降本增效或是寻求转型路径。