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.
第九十六条 在承运人责任期间内，托运人可以书面通知承运人中止运输、返还货物、变更卸货港或者将货物交给其他收货人，但是应当赔偿承运人因此遭受的损失。
[&:first-child]:overflow-hidden [&:first-child]:max-h-full"，推荐阅读下载安装 谷歌浏览器 开启极速安全的 上网之旅。获取更多信息
Раскрыты подробности о фестивале ГАРАЖ ФЕСТ в Ленинградской области23:00，详情可参考旺商聊官方下载
Get an HP M01-F300 Desktop on sale for $369.99.。爱思助手下载最新版本对此有专业解读
"iPad Air gives users more ways than ever to be creative and productive, offering powerful performance and incredible versatility to help them turn their ideas into reality," said Bob Borchers, Apple's vice president of Worldwide Product Marketing, in a statement. "With its blazing performance thanks to M4, incredible AI capabilities, and game-changing iPadOS 26 features, there's never been a better time to choose or upgrade to iPad Air."