Comparison between barycentric (triangular) dithering and Knoll’s algorithm using an 8-colour irregular palette. Left to right: barycentric, Knoll.
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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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Fresh off his Heated Rivalry success — a show so popular you can now book a stay at the Heated Rivalry cottage — the actor is hosting SNL this Saturday, and in the promo skit above he debuts his impressive range of accents in an "accent duel" with SNL's James Austin Johnson.，这一点在WPS官方版本下载中也有详细论述
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