发表机构
KAIST（韩国科学技术院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对关联应用组合维护的冗余与结构退化问题，提出超级库智能体方法，通过候选引导提取等技术，在WebGen-Bench等基准上减少冗余并避免结构退化。
AI 中文摘要
企业常开发和维护关联应用组合：可独立部署的代码库，共享大量领域逻辑、接口模式或操作约定。随着大语言模型（LLM）编码智能体越来越多地用于生成和维护这类软件，逐个应用的简单工作流会在代码库间复制共享逻辑，且长期智能体维护会积累冗余代码、死代码和结构退化。我们提出超级库智能体问题，即智能体按顺序生成N个关联应用组合，同时维护可跨应用复用的共享超级库组件。理论上，最小顺序框架可提取共享代码并将应用迁移到不断演进的库，但实际中存在提取召回率低、依赖迁移脆弱的问题。我们通过基于代码块摘要的候选引导提取、预提取代码库整合，以及利用提取轨迹和调用图信息的上下文感知迁移解决这些问题。在WebGen-Bench和PaperBench上，我们的方法在保留应用功能的同时，相比零样本方法显著减少了冗余和令牌占用（冗余度、令牌长度），避免了简单库构建带来的结构退化，还减少了代码行数（LOC）和模块依赖长度（MDL）。我们的代码可在此URL获取。
英文摘要
Organizations often develop and maintain portfolios of related applications: independently deployable codebases that share substantial domain logic, interface patterns, or operational conventions. As LLM coding agents are increasingly used to generate and maintain such software, a naive application-by-application workflow duplicates shared logic across codebases and allows prolonged agentic maintenance to accumulate verbosity, dead code, and structural erosion. We introduce the Super Library Agent problem, where an agent sequentially generates a portfolio of N related applications while maintaining a shared Super Library of reusable cross-application components. A minimal sequential scaffold can in principle extract shared code and migrate applications to the evolving library, but in practice suffers from low extraction recall and fragile dependency migration. We address these failures with candidate-guided extraction over code chunk summaries, pre-extraction codebase consolidation, and context-aware migration using extraction traces and call-graph information. Across WebGen-Bench and PaperBench, our method preserves application functionality while significantly reducing redundancy and token footprint (verbosity, token length) over zero-shot, and avoiding the structural erosion introduced by naive library construction, with additional reductions in LOC and MDL. Our code is available at https://github.com/sbigstar0310/super-library-agent.
CommentsFindings of the Association for Computational Linguistics: EMNLP 2026. Project page: https://sbigstar0310.github.io/super-library-agent/
