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.
let closed = false;
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The objects table contains the same bytes git would store on disk, and a set of SQL functions parse them into tree entries, commit metadata, and parent links that you can join against like any other table.。关于这个话题，im钱包官方下载提供了深入分析
// Hundreds of components = hundreds of enqueue calls
对场所进行检查的，经县级以上人民政府公安机关负责人批准，使用检查证检查；对确有必要立即进行检查的，人民警察经出示人民警察证，可以当场检查，并应当全程同步录音录像。检查公民住所应当出示县级以上人民政府公安机关开具的检查证。