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.
（一）核材料，是指需要管制的核材料，包括铀—235及含铀—235的材料和制品，铀—233及含铀—233的材料和制品，钚—239及含钚—239的材料和制品，法律、行政法规规定的其他需要管制的核材料，不包括铀（钍）矿石及其初级制品。
Yungblud has previously said he would like to grow the festival internationally.，更多细节参见同城约会
AI作为日常工具我主要用来当高效百度用，但放在工作中更多的是利用AI总结、归纳、整理的能力。它能帮我快速整理数据、总结文章。或者让它帮我干一些机械性、费时间（需要耐心完成）的一些工作。，详情可参考旺商聊官方下载
Credit: AdGuard
「圍繞這些整肅的宣傳主要是對國內、對中共內部發出的訊號，暗示無論是貪腐還是未能緊跟習近平偏好，都會付出沉重代價，而這些偏好可能會隨時改變。」，这一点在同城约会中也有详细论述