全国两会前的这段时间，全国人大代表、安徽省太和县现代农业科技试验示范基地党支部书记徐淙祥时常会骑上电动车去巡田。“1000多亩小麦，可马虎不得，尤其是前段时间又下雨又下雪的，要提前做好防护。”徐淙祥说。
史蒂夫很在意自己思考的性质与质量。他对自己期待极高，并努力让思考具有罕见的生命力、优雅与纪律。他的严苛与韧性把标准抬到了令人眩晕的高度。
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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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Panindre and colleagues have even attached the detection system to drones, which could help firefighters faced with pinpointing a blaze in a high-rise building: "These drones can actually go around the building and capture the location of the fire."