08版 - 本版责编:程 红

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declare -A SECRETS=(

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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对于普通人而言,2026年不是一个寻求“暴富神话”的年份,而是一个通过深耕细分领域、利用技术赋能、借势政策再平衡实现“阶梯式跃迁”的关键机会窗口。

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