36氪获悉，格力电器公告，第一大股东珠海明骏投资合伙企业（有限合伙）拟自公告披露之日起15个交易日后的3个月内，以大宗交易方式减持公司股份不超过11170.28万股，占公司剔除回购专用账户股份后的总股本的2%；减持原因为偿还银行贷款；股份来源为2020年1月23日通过协议转让自格力集团受让的股份。
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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A complex spec creates complex edge cases. The Web Platform Tests for streams span over 70 test files, and while comprehensive testing is a good thing, what's telling is what needs to be tested.
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