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研究：AGENTS.md文件可使AI编程Agent运行时间缩短29%、输出token减少17%
GateNews
BlockBeats 消息，3 月 3 日，据 1M AI News 消息，新加坡管理大学、海德堡大学、班贝格大学和伦敦国王学院的研究团队在 arXiv 发表论文，首次量化评估了仓库级配置文件 AGENTS.md 对 AI 编程 Agent 效率的影响。AGENTS.md 是一种存放在代码仓库根目录的指令文件，用于向 AI Agent 说明项目架构、构建命令、编码规范和操作约束，类似于 Anthropic Claude Code 的 CLAUDE.md 和 GitHub Copilot 的 copilot-instructions.md，目前已被超过 6 万个 GitHub 仓库采用。
研究团队在 10 个仓库的 124 个已合并 PR（代码改动均不超过 100 行）上使用 OpenAI Codex（gpt-5.2-codex）进行配对实验，分别在有和无 AGENTS.md 两种条件下运行。结果显示，存在 AGENTS.md 时中位数运行时间从 98.57 秒降至 70.34 秒（降幅 28.64%），中位数输出 token 从 2,925 降至 2,440（降幅 16.58%），任务完成行为无显著差异（Wilcoxon signed-rank test, p < 0.05）。
研究者指出，AGENTS.md 将 Agent 指导从「短暂的提示词」转变为「版本控制、可审查、协作维护的配置工件」，建议开发团队将其作为标准实践纳入仓库。局限性方面，该研究仅测试了 OpenAI Codex 单一 Agent，样本限于小规模 PR，且未进行全面的代码正确性评估。
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