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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Instead of forcing your application into a prescriptive template like Clean or Hexagonal Architectures, get back to basics and use patterns from Modular Software Design. Divide the application into independent modules, each containing business logic representing a specific process. For modules with complex business logic, extract the infrastructure-related code into separate Infrastructure-Modules. This will enable you to build an application characterized by low cognitive load, high maintainability, and high extensibility.
我代表二十届中央纪律检查委员会常务委员会向第五次全体会议作工作报告，请予审议。
You can contact or verify outreach from Sarah by emailing [email protected] or via encrypted message at sarahperez.01 on Signal.