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.
63-летняя Деми Мур вышла в свет с неожиданной стрижкой17:54
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I asked a more data-science-oriented followup prompt to test Opus 4.5’s skill at data-sciencing:
At the same time, Dutch economic output or GDP per capita – relative to each person – remains both among the highest in Europe, and close to top of the OECD (Organisation for Economic Co-operation and Development) member states of developed economies.
What if you create a truly unique routing profile that's wildly different from the common ones for which shortcuts were pre-calculated? The system is smart. If it detects that too many shortcuts (~50, for example) need on-the-fly recalculation and deviate significantly, it might determine that falling back to the original, comprehensive A* algorithm for the entire route would actually be faster than doing many small, heavily modified A* calculations.