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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[&:first-child]:overflow-hidden [&:first-child]:max-h-full"，更多细节参见Safew下载
UMAP: 2-10x faster than Rust’s fast-umap, 9-30x faster than Python’s umap，更多细节参见91视频
On top of making documentaries (and being famous for Jiggle Jiggle), Theroux is known for his Louis Theroux Interviews... podcast in which he interviews stars like Sean Penn and Florence Pugh. Prior to that, he did stories on conspiracy theories, UFOS and the porn industry, topics that he said were once niche but are now driving the internet and culture.
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