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In recent years, LLMs have shown significant improvements in their overall performance. When they first became mainstream a couple of years before, they were already impressive with their seemingly human-like conversation abilities, but their reasoning always lacked. They were able to describe any sorting algorithm in the style of your favorite author; on the other hand, they weren't able to consistently perform addition. However, they improved significantly, and it's more and more difficult to find examples where they fail to reason. This created the belief that with enough scaling, LLMs will be able to learn general reasoning.
，这一点在体育直播中也有详细论述
这是月之暗面“模型即Agent”的逻辑。目前，绝大多数Agent产品的智能上限仍由模型能力决定，只有拥有强大、可控、具有上下文和复杂推理能力的自研基座模型，才能支撑起用户理想中的“Agent”，而这也更符合Kimi长期聚焦长文本的优势。
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