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.
Doing this "in one pass" sounds easy, but this can cause your
从2026年1月开始，AI风险到底算不算承保范围将被保险业写进条款。Verisk推动的AGI排除背书以2026年1月开始生效，把一块长期模糊的责任边界变成行业文本。，详情可参考91视频
Source: Computational Materials Science, Volume 267
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Spats, theatrics and a walkout - How the Pam Bondi hearing unfolded，推荐阅读51吃瓜获取更多信息
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