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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.
但据报道，随着全球 AI 军备竞赛加剧与模型训练成本激增，Anthropic 坚持单方面的严苛限制已导致公司在与 OpenAI、Google 等巨头的商业博弈中面临研发进度落后的风险。，这一点在heLLoword翻译官方下载中也有详细论述
FT Edit: Access on iOS and web，这一点在WPS下载最新地址中也有详细论述
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