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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.
甚至到了2026年的今天，他發現家人投票仍要搶在開門第一時間去排隊，只因「深怕自己投票被看見」。，详情可参考heLLoword翻译官方下载
Гангстер одним ударом расправился с туристом в Таиланде и попал на видео18:08
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More posts 26 Feb 2026 8 min read
上世纪，福建当地一家企业斥巨资买断了三明市万寿岩的开采权，这一后来被誉为“南方周口店”的史前遗址，一度面临挖掘机和炸药包的威胁。。关于这个话题，搜狗输入法2026提供了深入分析