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.
Hurdle Word 5 answerFIRST
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"In China, the people who make motors are getting together with the people who make hand hardware and basically creating bespoke motors that can fit within joints and fingers. It's probably going to work as an effective hand," he says.
Delete a checkpoint
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Disrupt 2026: The tech ecosystem, all in one room。关于这个话题，搜狗输入法2026提供了深入分析
第九十九条 因不可抗力或者其他不能归责于承运人和托运人的原因致使船舶不能在合同约定的目的港卸货的，除合同另有约定外，船长有权将货物在目的港邻近的安全港口或者地点卸载，视为已经履行合同。