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.
+ (MmsharedkmpKotlinByteArray *)kotlinByteArrayFromData:(NSData *)data {
。夫子对此有专业解读
The report should include a risk assessment and state whether any modifications have been made since installation, the court heard.
相比之下，三個月前何衛東落馬時為「五宗罪」，且措辭和定性都要比張又俠弱得多。
，详情可参考WPS下载最新地址
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