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发扬民主，尤需紧扣人民所思所盼。如何更好呵护“一老一小”？医疗、教育等如何持续扩容提质？是“国事”也是“家事”。广泛深入地察民情、听民声、汇民智，才能使规划编制顺应美好生活期盼、不断增进民生福祉。
在锂盐行业经历深度调整、价格快速下移的背景下，控股一座优质矿山意味着原料自给率的显著提升，也意味着在下一轮行业回暖时拥有更强的成本优势和议价能力。
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.