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.
1. “OpenAI联邦”的崛起：分裂即创新随着OpenAI向商业化产品巨头转型（用户破亿，团队膨胀），其内部的科研纯粹性受到挑战。于是，一场静悄悄的“出走潮”催生了估值千亿的“OpenAI联邦”：，详情可参考新收录的资料
。新收录的资料对此有专业解读
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“부자 3대 못 간다”는 건 옛말，这一点在新收录的资料中也有详细论述