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.
近日，《广东新闻联播》记者探访了 OPPO 手机生产一线，释出了即将发布的折叠屏新品 Find N6 的真机视频，显示其折痕「浅到几乎看不见」。，这一点在一键获取谷歌浏览器下载中也有详细论述
«Неужели ЕС — вопреки очевидным фактам, интересам кубинцев и европейской безопасности — намерен поддерживать позорное существование PDCA и продолжать свою скандальную политику сотрудничества с коммунистической Гаваной?» — высказался политик.，这一点在WPS下载最新地址中也有详细论述
Then $75 per month. Complete digital access to quality FT journalism on any device. Cancel anytime during your trial.，这一点在WPS官方版本下载中也有详细论述
The radio silence that accompanies re-entry went on far longer than normal. Millions watched on TV, many convinced that all was lost.