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.
“出乎意料的是，每一个国家的代表都表达了最积极的意愿和全力的支持。”宁光在一一拜访上合组织国家驻华使节过程中，既感意外又颇感振奋，“健康是全人类共同的追求，上合组织各国在代谢性疾病防控领域不仅积累了丰富的本土经验，对中国经验和成果也十分了解。”
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14:42, 27 февраля 2026Мир