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await WebAssembly.instantiateStreaming(bytecode, imports);
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他向BBC中文表示,問卷寫法會讓居民認為「時間」作為唯一考慮因素,甚至「慢慢令到想選擇不同計劃的人有一個對立面」。他又指,問卷第一條問題是向居民查詢「政府應考慮哪些重要原則」,質疑為何要居民要代替政府思考,而目前居民亦無政府所擁有的資訊,難以有客觀答案。
,这一点在搜狗输入法2026中也有详细论述
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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