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.
Раскрыты подробности похищения ребенка в Смоленске09:27
。关于这个话题，WPS官方版本下载提供了深入分析
Copyright © 1997-2026 by www.people.com.cn all rights reserved
NHS England and counterparts in the rest of the UK are seeking to increase supplies from four other manufacturers that supply the NHS.
，更多细节参见下载安装 谷歌浏览器 开启极速安全的 上网之旅。
Фото: Alina Smutko / Reuters，推荐阅读WPS下载最新地址获取更多信息
（三）国务院财政、税务主管部门规定的其他情形。