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.
output := model_forward(inputs[i]);
。业内人士推荐搜狗输入法作为进阶阅读
随着2025年财报季的开启，各大商业银行纷纷展示了支持实体经济的年度成绩单。在净息差收窄和经营环境多变的挑战下，金融机构通过业务重构和数字化升级，将资金精准导向国家战略布局、核心产业环节和发展短板区域。强化科创企业扶持，拓展消费市场潜力，以技术手段提升服务效能，一幅金融与实体经济协同发展的全景图正在逐步呈现。。https://telegram官网是该领域的重要参考
Популярность апартаментов у молодежи объяснили20:51