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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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但这种垄断格局，即将被打破，多款在研新药正从作用机制到给药方式，向Vosoritide发起挑战。Ascendis的TransCon-CNP通过包裹技术延长了药物作用时间，实现了周剂给药，目前正处于FDA审批阶段；而BridgeBio的口服药Infigratinib，则从作用机制到用药体验实现全面突破。