Adsorption of CO oxidation intermediates on Ag<img class="glyph" src="https://sdfestaticassets-us-east-1.sciencedirectassets.com/shared-assets/16/entities/sbnd" />Au nanoparticles across quantum-confined to bulk-like size regimes

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business process of a large customer.

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Additional reporting by Ema Sabljak, BBC England Data Unit

When shoes slide across a floor, wave-like deformations of the sole can generate squeaking. The pitch of the squeak depends on the rate at which deformations are generated.

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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.,详情可参考safew官方下载