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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.
143 亿美元买下 Scale AI 近半股份，把 Alexandr Wang 拉进来直接向自己汇报；四处挖角 OpenAI、Anthropic、Google 的核心骨干。。关于这个话题，体育直播提供了深入分析
“这些其实都不是新话题，但都需要持续跟进、不断创新。水产养殖不能只算产量账，更要算生态账、安全账。”陈阳说，“怎么让行业在增产的同时不透支水域承载力？怎么从投入品源头把住安全质量关？这些既是技术问题，也关系到政策，需要提出更有针对性的建议。”