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.
第七十条 托运人托运危险货物，应当按照有关海上危险货物运输的规定妥善包装，作出危险品标志和标签，并将货物正式名称和性质以及应当采取的预防危害措施、应急处置措施书面通知承运人；托运人未通知或者通知有误的，承运人可以在任何时间、任何地点根据情况需要将货物卸下、销毁或者消除危险性，而不承担赔偿责任。托运人对承运人因运输此类货物所受到的损害，应当承担赔偿责任。
，更多细节参见体育直播
India disrupts access to popular developer platform Supabase with blocking order，详情可参考体育直播
第二百四十二条 下列各项可以作为保险标的：。业内人士推荐体育直播作为进阶阅读
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