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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.
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产品本身获得了更多小镇青年的喜欢之外，基础建设的日渐完善，则成为了强有力助攻。数据显示，截至2024年，全国县域公共充电桩数量已达到43.77万台，在全国占比达13.16%。
随着空间计算设备的普及，未来的AI视频模型很可能进行3D场景的探索。51吃瓜是该领域的重要参考