This sounds reasonable until you see how easily it goes wrong:
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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.
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Also note the use of _call.call(_toString, original) rather than simply original.toString(). This is because original.toString might itself be hooked by the time spoof is called. By holding cached references to Function.prototype.call and Function.prototype.toString at the very beginning of the script (before any page code runs), and invoking them via those cached references, the spoof function is immune to any tampering that might have happened in the interim. It’s eating its own tail in the most delightful way.
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