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.
Российский теннисист Андрей Рублев проиграл в полуфинале турнира в Дубае голландцу Таллону Грикспуру. Об этом сообщает корреспондент «Ленты.ру».
На Западе подчинили рой насекомых для разведки в интересах НАТО08:43，详情可参考同城约会
"If you've got nuclear power for a base, but you've got no way of getting people and equipment there, then it's not much use," he added.。搜狗输入法2026对此有专业解读
«Ты будешь меня слушать»Россиянок лишают права тратить собственные деньги. Как устроена их жизнь?26 мая 2025
Continue reading...。WPS官方版本下载是该领域的重要参考