第一百一十八条 就旅客人身伤亡或者行李灭失、损坏通过诉讼、仲裁等方式对承运人提起的任何赔偿请求，无论请求人是否为合同的一方，也无论是根据合同或者侵权行为提出请求，均适用本章关于承运人的抗辩理由和限制赔偿责任的规定。
Россиянам пригрозили тюрьмой за неправильную пропискуЮрист Русяев: За фиктивную регистрацию собственник жилья рискует сесть в тюрьму
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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.
Some of the objects discovered have already been shown to the public.