"中文电子病历国际疾病分类(ICD)诊断编码评测依托第二十四届中国计算语言学大会(CCL)举办。该评测聚焦于自然语言处理技术在智能医疗领域的应用,旨在从真实脱敏的电子病历文本中自动分析关键临床表征,实现主诊断及其他诊断ICD编码的精准预测与分配,从而辅助临床医生与专业编码员提升编码工作的准确性和效率。本次评测在阿里云天池平台进行,获得了学术界与工业界的广泛关注和积极参与。数据显示,共有445支队伍报名参赛,其中A榜和B榜分别有85支和36支队伍成功提交了有效结果。最终,8支表现优异的队伍受邀撰写并分享了其技术报告,为推动该领域的技术进步与方法创新贡献了宝贵经验。本次评测的详细信息可参见相关发布页面。"

With open-source projects growing in size and complexity, manual compilation becomes tedious and error-prone, highlighting the need for automation to improve efficiency and accuracy. However, the complexity of compilation instruction search and error resolution makes automatic compilation challenging. Inspired by the success of LLM-based agents in various fields, we propose CompileAgent, the first LLM-based agent framework dedicated to repo-level compilation. CompileAgent integrates five tools and a flow-based agent strategy, enabling interaction with software artifacts for compilation instruction search and error resolution. To measure the effectiveness of our method, we design a public repo-level benchmark CompileAgentBench, and we also design two baselines for comparison by combining two compilation-friendly schemes. The performance on this benchmark shows that our method significantly improves the compilation success rate, ranging from 10% to 71%. Meanwhile, we evaluate the performance of CompileAgent under different agent strategies and verify the effectiveness of the flow-based strategy. Additionally, we emphasize the scalability of CompileAgent, further expanding its application prospects. The complete code and data are available at https://github.com/Ch3nYe/AutoCompiler.