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.
We leveraged this same approach here using the same OpenAPI JSON to generate boilerplate C# code for registering and wiring up all endpoints within the serverless codebase. Interfaces are generated for each controller with a function for each action. Functions only need to be implemented as necessary for the systems that are required in the offline game.。关于这个话题，Line官方版本下载提供了深入分析
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