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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.
这两种论调看似矛盾，其实只是“转型阵痛”的一体两面。传统软件恐慌等于旧价值体系的瓦解，英伟达疑虑等于新价值体系的不确定性，两者共同指向一个中间状态：在“Agent经济学”被验证之前，没有安全资产，只有“相对不贵的押注”。