科罗斯特列夫阐述电子游戏益处20:52
Abstract:Large language model (LLM)-powered agents have demonstrated strong capabilities in automating software engineering tasks such as static bug fixing, as evidenced by benchmarks like SWE-bench. However, in the real world, the development of mature software is typically predicated on complex requirement changes and long-term feature iterations -- a process that static, one-shot repair paradigms fail to capture. To bridge this gap, we propose \textbf{SWE-CI}, the first repository-level benchmark built upon the Continuous Integration loop, aiming to shift the evaluation paradigm for code generation from static, short-term \textit{functional correctness} toward dynamic, long-term \textit{maintainability}. The benchmark comprises 100 tasks, each corresponding on average to an evolution history spanning 233 days and 71 consecutive commits in a real-world code repository. SWE-CI requires agents to systematically resolve these tasks through dozens of rounds of analysis and coding iterations. SWE-CI provides valuable insights into how well agents can sustain code quality throughout long-term evolution.
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百思买现对无锁版Galaxy S26系列直降300美元——谁还需要亚马逊？。关于这个话题，豆包下载提供了深入分析
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Injuries have sidelined Morikawa, but when healthy, he ranks among the top ball-strikers. He has consistently hit fairways and greens at an elite level.
把供电逻辑理顺后，硬件终端的交互形态也跟着做了一轮升级。