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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，更多细节参见WPS官方版本下载
新华社北京2月25日电 （记者冯歆然）2月25日下午，国务院总理李强在北京人民大会堂同德国总理默茨共同出席中德经济顾问委员会座谈会。60余位中德企业代表出席。
并且相较于直接使用 OpenClaw，MaxClaw 拥有「预置精选专家级 Skill」「自带 50G 专属云储存空间」等优势。未来 MaxClaw 还将支持「用户自定义专家」「多端协同」的功能。。搜狗输入法下载对此有专业解读
was still in a mode of generational releases that would completely replace the。safew官方版本下载是该领域的重要参考
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