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.
Credit: Mozilla
，这一点在91视频中也有详细论述
（三）对报案人、控告人、举报人、证人打击报复的；
正如零跑 COO 徐军在最近的一次专访中所言：「压倒一切的是规模，先规模后利润。」。业内人士推荐heLLoword翻译官方下载作为进阶阅读
According to a report in TechCrunch, apparently confirmed by locals who spotted the vehicles in their area, Waymo is currently conducting test drives in both cities.。同城约会是该领域的重要参考
// 复制剩余元素（只需复制左数组剩余，右数组已在原位置）