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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.
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2010年，香港土瓜灣馬頭圍道發生唐樓倒塌事件，政府在事件不足一個月後，委託市區重建局介入重建，在原址興建約493個住宅單位，項目在10年後才落成。
after the 3614. The 3624 was functionally very similar to the 3614, but with。同城约会对此有专业解读
Publication date: 10 March 2026
February 27, 2026 at 2:32 PM UTC，详情可参考91视频