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我在《把离线AI智能体装进口袋里》(The Dawn of Offline AI Agents in Your Pocket)一文中对此进行了详细阐述。但文章中的示例更像是Demo,而非生产解决方案。像 Gemma 3n 这样的模型虽然能够很好地处理函数调用,但它们体积过大:无法集成到应用程序包中,需要单独下载,即使在旗舰机型上推理速度也很慢。在低端设备上,它们根本无法运行。而较小的型号则经常出现故障,难以记住工具。
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I wanted to test this claim with SAT problems. Why SAT? Because solving SAT problems require applying very few rules consistently. The principle stays the same even if you have millions of variables or just a couple. So if you know how to reason properly any SAT instances is solvable given enough time. Also, it's easy to generate completely random SAT problems that make it less likely for LLM to solve the problem based on pure pattern recognition. Therefore, I think it is a good problem type to test whether LLMs can generalize basic rules beyond their training data.
У нового iPhone 17e есть пять крупных отличий от предшественника. Об этом сообщает издание 9to5Mac.。关于这个话题,同城约会提供了深入分析