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.
批評者指出，對郭父的定罪和判刑，標誌著港府對海外政見人士的打壓已擴展到其家人，做法越來越像中國大陸的高壓路線。。业内人士推荐雷电模拟器官方版本下载作为进阶阅读
经过两次车主爽约，我对返程坐顺风车已经不抱希望。情急之下，只能赶紧打开铁路购票App。运气还算不错，返程当天的二等座只剩3张，我手忙脚乱地选座、填信息、提交订单，付款成功的那一刻，悬着的心才落地。还好没被顺风车耽误返程，不然真不知道该怎么赶回去上班。。业内人士推荐夫子作为进阶阅读
Ранее украинский президент Владимир Зеленский заявил, что конфликт на Украине может быть завершен в следующем году. Он также отметил, что говорить о возвращении потерянных Украиной территорий очень сложно.
pixel[0] = pixel[0] 0.04045f ? powf((pixel[0] + 0.055f) / 1.055f, 2.4f) : pixel[0] / 12.92f;