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.
Like this clue numberThe answer is Odd.
«Сегодня столичный регион будет ощущать влияние уходящего на юго-восток циклонического вихря. В такой ситуации ожидается преимущественно облачная погода, местами пройдет небольшой снег, мокрый снег, на дорогах и тротуарах гололедица», — поделился информацией синоптик. Столбики термометров в Москве, продолжил он, покажут от нуля до плюс двух градусов Цельсия, а в Подмосковье — от нуля до плюс трех. Северный ветер будет дуть со скоростью три-восемь метров в секунду. Атмосферное давление в течение дня повысится до 744 миллиметров ртутного столба, что ниже нормы.，详情可参考WPS下载最新地址
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大众增程器打了“半成品交付”们的脸。关于这个话题，同城约会提供了深入分析
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