ETH Zurich：上下文让AI变笨且费钱

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这项研究探讨了仓库级上下文文件（如 AGENTS.md）对AI 编程代理解决实际工程任务的影响。研究人员通过构建名为 AGENTBENCH 的新基准测试发现，提供此类文件往往会降低任务成功率，并使推理成本增加 20% 以上。实验表明，尽管代理能够遵循这些文件中的指令，但过多的冗余要求会导致其进行过度探索和测试，反而增加了处理难度。相比之下，由开发人员编写的精简上下文文件表现略好于模型自动生成的文件。因此，作者建议目前的上下文文件应保持简洁，仅包含必要的工具使用说明，而非宽泛的代码库概述。该研究揭示了当前行业建议与实际效果之间的差距，为优化 AI 代理的提示词工程提供了依据。Evaluating AGENTS.md: Are Repository-Level Context Files Helpful for Coding Agents?

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ETH Zurich：上下文让AI变笨且费钱

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