Nasa administrator Jared Isaacman told a media briefing that he was adding an extra step to the Artemis programme because he did not want such long gaps between launches.
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This is a well-known browser security technique. In JavaScript, calling .toString() on a native browser function returns "function appendBuffer() { [native code] }". Calling it on a JavaScript function returns the actual source code. So if your appendBuffer has been monkey-patched, .toString() will betray you; it’ll return the attacker’s JavaScript source instead of the expected native code string.
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.