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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Can these agent-benchmaxxed implementations actually beat the existing machine learning algorithm libraries, despite those libraries already being written in a low-level language such as C/C++/Fortran? Here are the results on my personal MacBook Pro comparing the CPU benchmarks of the Rust implementations of various computationally intensive ML algorithms to their respective popular implementations, where the agentic Rust results are within similarity tolerance with the battle-tested implementations and Python packages are compared against the Python bindings of the agent-coded Rust packages:
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