当 Meta 宁愿花费天价也要扶持出第二、第三个供应商时，意味着 AI 算力市场从英伟达“一家独大”向“多强争霸”的历史性拐点，已经真正到来。
ВСУ запустили «Фламинго» вглубь России. В Москве заявили, что это британские ракеты с украинскими шильдиками16:45
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2025年，香港恒生指数全年累计涨幅高达27.77%，创下自2017年以来的最佳年度表现，仅次于深成指30%的年内涨幅。恒生科技指数同样表现亮眼，全年累计上涨23.45%，为自2020年设立以来的最佳年度表现。恒生国企指数年内也上涨22.27%，显示出港股整体市场的强劲复苏势头。，更多细节参见下载安装 谷歌浏览器 开启极速安全的 上网之旅。
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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