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.
Российская разведка заявила о планах Запада вооружить Киев ядерной бомбой. В Кремле возмущены, Медведев угрожает ядерным ударом24 февраля 2026
В конце сентября прошлого года министр энергетики России Сергей Цивилев заявил, что только половина суммарных запасов нефти в России является рентабельной с точки зрения геологоразведки.。关于这个话题，51吃瓜提供了深入分析
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I really like this approach and find it reassuring about OSTree’s ability to manage service configurations without forcing us to never modify them.。关于这个话题，safew官方版本下载提供了深入分析
But let’s be honest: how many people actually remember its syntax?