AI 中文总结
PRAXIS框架通过模拟人类开发工作流提取代码依赖图上的默会知识，提升智能体领域代码生成性能，优于现有方法且可适配多种智能体框架与大语言模型
AI 中文摘要
大语言模型智能体在通用软件工程任务上已表现出较强性能，但在领域代码生成方面仍存在不足。我们将根本原因归结为智能体缺乏默会知识，包括开发者通过实践内化却从未记录的领域特定业务规则、接口契约和操作惯例。这类知识深埋在领域代码之下，分散在代码实体及其依赖关系中，对不具备该知识的智能体而言不可见，这些特性使得默会知识本质上难以检索或学习。本研究提出PRAXIS框架，使智能体能够系统地提取、表示和复用默会知识以实现领域代码生成。PRAXIS通过在目标代码库中模拟人类开发工作流来获取默会知识，将其提炼为按代码依赖图组织的结构化单元，并在代码交互时主动呈现给智能体。大量实验表明，PRAXIS的性能优于配备强大智能体搜索能力的最先进智能体，以及基于经验和技能的方法。该方法可无缝集成到各类智能体框架和大语言模型中，实现一致的性能提升，且支持持续演进，随着实践积累，性能稳步提升。
英文摘要
LLM agents have achieved strong performance on general software engineering tasks, yet struggle with domain-specific code generation. We identify the root cause as the agent's lack of tacit knowledge, including domain-specific business rules, interface contracts, and operational conventions that developers internalize through practice but never document. This knowledge is deeply buried beneath the domain code, dispersed across code entities and their dependency relations, and invisible to the agent that lacks it. These properties make tacit knowledge inherently difficult to retrieve or learn. In this work, we propose PRAXIS, a framework that enables agents to systematically extract, represent, and reuse tacit knowledge for domain code generation. PRAXIS acquires tacit knowledge by simulating human development workflows within the target codebase, distills it into structured units organized on the code dependency graph, and proactively surfaces it to the agent at the point of code interaction. Extensive experiments demonstrate that PRAXIS outperforms state-of-the-art agents equipped with powerful agentic search capabilities, as well as experience-based and skill-based methods. The approach integrates seamlessly into various agent frameworks and LLMs with consistent performance improvements, and supports continual evolution with performance steadily scaling as practice accumulates.
