Comparative analysis of plasticity-based GND density estimation methods in crystal plasticity finite element models

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对 OpenAI 而言,拓展政府军工订单是商业化层面的理性选择。在算力成本高昂、C端增长面临挑战的当下,国防预算意味着稳定的资金流与算力支撑。

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学问从教授开始,这一点在搜狗输入法中也有详细论述

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.

08:32, 4 марта 2026Бывший СССР

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