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.
移民議題曾是特朗普的政治強項，但他在明尼阿波利斯的執法行動導致聯邦探員槍殺兩名美國公民，嚴重削弱了特朗普的支持度。
第四十六条 违反有关法律法规关于飞行空域管理规定，飞行民用无人驾驶航空器、航空运动器材，或者升放无人驾驶自由气球、系留气球等升空物体，情节较重的，处五日以上十日以下拘留。。业内人士推荐服务器推荐作为进阶阅读
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直播中，小米安全专家详细拆解了事故调查的完整流程：一起交通事故或火灾事故发生后，交警、消防部门会首先封闭现场、封存车辆，随后调取各类相关数据和信息，完成现场勘查后，将召集专家进行分析研判，部分复杂事故还需经过鉴定实验，最终才能得出技术结论，这一过程需要一定时间，无法快速完成。。heLLoword翻译官方下载对此有专业解读