如果把这部电影看作香港城市记忆的一次回收，它的意义会更清楚。《夜王》不试图为夜总会立碑，也没有把它洗白成温情乡愁。它只是承认：香港确实有过这样的夜晚，有过这样的空间，有过一套依赖灰度与情义运作的社会机制，而当那套机制被替换，人需要面对的不只是行业的消失，还有自我认同的漂移。曾经熟悉的城市，在某一天会变得陌生。曾经相信的规则，在某一天会突然失效。
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5年来，哈法亚油田持续推动工艺体系迭代升级，依托天然气处理厂等绿色项目，持续提升清洁能源供给与减排成效，同步推进植树养护、环境监测和生物多样性保护，严格落实湿地环境保护要求，以可见可感的生态实践增进当地认同，凝聚共享发展共识。
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.