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.
2018年，习近平总书记在宜昌考察时强调，要坚持把修复长江生态环境摆在推动长江经济带发展工作的重要位置，共抓大保护，不搞大开发。
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Мощный удар Израиля по Ирану попал на видео09:41。业内人士推荐同城约会作为进阶阅读
Nature, Published online: 25 February 2026; doi:10.1038/d41586-026-00599-5
drop-newest: Discards incoming data when full. Useful when you want to process what you have without being overwhelmed.，推荐阅读搜狗输入法2026获取更多信息