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这项由南京大学与快手技术联合开展的研究，于2026年4月以预印本形式发布在arXiv平台，编号为arXiv:2604.18224。这是一项关于如何更科学地评价人工智能写网页代码能力的基础性研究，感兴趣的读者可通过该编号查询完整论文。

从目前测试结果来看，DeepSeek V4 的百万上下文长度有效性百分比不是很高，幻觉率较高导致不管在简单还是较困难的任务中都有可能出现低级错误，导致表现不稳定。在 Claude Code 中的代码审查阶段，有时要消耗三分之一到一半的时间来改代码。