一句话：提出对多智能体 LLM 结对编程的系统研究，通过外化意图与工具迭代验证，使生成工件可信、可审计且易维护。
基于 LLM 的编程智能体已广泛用于生成代码、测试和文档，但其输出可能看似合理却偏离开发者意图，且难以在持续演化的项目中提供足够的评审证据，导致 LLM 结对编程工作流如何保证工件可靠、可审计和可维护尚不明确。本博士研究提出系统研究多智能体 LLM 结对编程：外化开发者意图，并利用开发工具进行迭代验证。研究计划包含三项工作：将非形式问题陈述转化为符合标准的需求与形式规约；利用求解器反例等自动反馈精化测试与实现；在保持已验证行为的前提下支持重构、API 迁移和文档更新等维护任务。预期成果是更清晰地理解多智能体工作流何时提升信任，并为构建面向真实开发的可靠编程助手提供实践指导。
LLM-based coding agents are increasingly used to generate code, tests, and documentation. Still, their outputs can be plausible yet misaligned with developer intent and provide limited evidence for review in evolving projects. This limits our understanding of how to structure LLM pair-programming workflows so that artifacts remain reliable, auditable, and maintainable over time. To address this gap, this doctoral research proposes a systematic study of multiagent LLM pair programming that externalizes intent and uses development tools for iterative validation. The plan includes three studies: translating informal problem statements into standards-aligned requirements and formal specifications; refining tests and implementations using automated feedback, such as solver-backed counterexamples; and supporting maintenance tasks, including refactoring, API migrations, and documentation updates, while preserving validated behavior. The expected outcome is a clearer understanding of when multi-agent workflows increase trust, along with practical guidance for building reliable programming assistants for real-world development.