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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A panel of voters normally chooses between six and eight performers to be inducted from the nominations.
With its climate change angle and unlikely friendships, Arco recalls two of 2024's animated standouts: Flow and The Wild Robot. But it also forges an identity of its own thanks to its stunning 2D animation, which plays like a combination of the styles of Jean Giraud (aka Mœbius) and the films of Studio Ghibli. As I wrote in my review, "In a mainstream animation landscape dominated by 3D-animated films, Arco's visuals are a testament to the enduring power of 2D work, as well as French filmmakers' commitment to the medium. If you love animation, run, don't walk — or better yet, fly by rainbow — to catch it."* — B.E.