The entire pipeline executes in a single call stack. No promises are created, no microtask queue scheduling occurs, and no GC pressure from short-lived async machinery. For CPU-bound workloads like parsing, compression, or transformation of in-memory data, this can be significantly faster than the equivalent Web streams code – which would force async boundaries even when every component is synchronous.
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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第五十三条 纳税人实施不具有合理商业目的的安排而减少、免除、推迟缴纳增值税税款，或者提前退税、多退税款的，税务机关可以依照《中华人民共和国税收征收管理法》和有关行政法规的规定予以调整。。关于这个话题，Line官方版本下载提供了深入分析
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2026-02-28 00:00:00:03014271810http://paper.people.com.cn/rmrb/pc/content/202602/28/content_30142718.htmlhttp://paper.people.com.cn/rmrb/pad/content/202602/28/content_30142718.html11921 柬埔寨国王西哈莫尼和太后莫尼列来华