С российской кризисной отрасли решили собрать еще миллиарды рублей

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2026-03-03 00:00:00:0本报记者 高 炳 贾丰丰3014314010http://paper.people.com.cn/rmrb/pc/content/202603/03/content_30143140.htmlhttp://paper.people.com.cn/rmrb/pad/content/202603/03/content_30143140.html11921 西延高铁,激发红色旅游新动能(新春走基层)

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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.

三年研发之路,资金问题始终是悬在头顶的利剑。即便如此,波波不想找外部投资,也不愿预支发行的预付款,她始终觉得“拿人手短,会失去创作的自由”。

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src/utils.ts — auto-resolved

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