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.
While this is immediately effective, the random perturbations introduce a disturbing texture that can obfuscate details in the original image. To counter this, we can make some smart choices on where and by how much to perturb our input image in an attempt to add some structure to our dither and preserve some of the lost detail.
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从Forever 21到GAP，再到GUESS，美式服饰品牌在中国陷入困境并非只有个案。市场并没有拒绝外资品牌，而是在更高效率与更强内容表达的双重要求下重新筛选玩家。对于GUESS而言，这次退出未必是终点。但下一次回归若只是形式变化而缺乏真正的产品与叙事升级，那么中国市场对它的耐心，恐怕不会再像从前那样充足。
highWaterMark: 10,