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.
当年这话听起来像极了新势力惯用的画饼话术，但在 A10 身上，它终于有了扎实的落脚点。
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A developer wanting to use a new Web API must first understand it from a JavaScript perspective, then translate it into the types and APIs that are available in their source language. Toolchain developers can try to manually translate the existing web documentation for their language, but that is a tedious and error prone process that doesn’t scale.
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神韻藝術團於2006年在美國紐約州北部創立，其精心編排的舞蹈表演包含針對中國共產黨的隱晦批評。近年來，這支舞蹈團也面臨虐待員工的指控，但他們予以否認。。关于这个话题，safew官方下载提供了深入分析