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.
16:05, 27 февраля 2026Путешествия
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The astronaut who experienced a medical issue in space last month has identified himself as the sick member of his crew prompting an evacuation from the International Space Station.
2月26日，湖北省孝感市云梦田律农事综合服务中心智能化育秧室，员工们在忙着选种、育秧，为春耕春种做准备。图为中心员工在查看培育的秧苗。