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.
* 时间复杂度：O(n)，空间复杂度：O(1)。业内人士推荐WPS官方版本下载作为进阶阅读
如果说财务压力是明面上的焦虑，那么规模效应缺失带来的技术困境，则是蔚来心底更深的焦虑。在半导体行业，没有规模就没有成本优势，没有成本优势就没有生存空间——这是铁律。。下载安装 谷歌浏览器 开启极速安全的 上网之旅。是该领域的重要参考
./with-1password.sh node server.js，详情可参考safew官方版本下载