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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.
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但从2024 年开始,这套运行多年的逻辑,正在悄然失效。时间走到 2026 年,曾经熟悉的周期剧本彻底改写,整个存储行业,正同时发生五大关键变化。在此转变之下,存储芯片产业正经历一场从“规模驱动的周期博弈”向“技术驱动的价值竞争”的根本性转向。,推荐阅读heLLoword翻译官方下载获取更多信息