用可版本控制的契约构建科学智能体，让科学家专注判断而非反复调试。
Fantastic Scientific Agents and How to Build Them: AgentBuild for Rietveld Refinement
- 以科学家编写的契约作为构建规范，驱动自动化代理生成
- 在锂镧锆氧数据上实现4小时扫描的前沿案例突破，暴露工作流边界
- 契约可复用，模型升级后只需重跑，无需重新设计
随着科学工作流从确定性程序转向基于大语言模型的智能体，现有开发方法如微调、强化学习和提示即用，反而掩盖了科学家的判断力。本文提出将智能体构建视为工作流的一个阶段，引入AgentBuild：科学家编写一份版本可控的契约，包含评分标准、难度分级课程和外部知识库。一个基于评分的裁判代理驱动元优化编码代理，在限定范围内迭代修改智能体，使构建过程自动完成，而非依赖科学家的主观判断。我们在GSAS-II系统中实现该方法，通过MCP和A2A接口对X射线衍射数据进行里特维尔德精修，从零开始构建智能体，逐步通过锂镧锆氧（LLZO）信号噪声梯度测试，最终达到4小时扫描这一前沿案例。该契约不仅评估拟合可信度，还评估轨迹覆盖范围，当超出工作流能力时，判定为契约失败而非拟合失败。随着基础模型演进，只需重新运行AgentBuild即可实现再调优，而无需重建，科学家撰写的契约成为持久资产。
原文摘要 · Abstract (English)
As scientific workflows shift from deterministic executables to LLM-based agents, the development practices on offer, such as fine-tuning, reinforcement learning, and prompt-and-go, bury the scientist's judgment. We propose treating agent construction as a workflow stage and introduce AgentBuild, which builds a scientific agent from a contract the scientist authors. The contract is a version-controlled rubric, a difficulty-graded curriculum, and a curated external knowledge base. A rubric-driven judge gates a meta-optimizer coding agent that edits the agent within a declared boundary, so the build compiles the agent, not the scientist's judgment. We instantiate this for Rietveld refinement of X-ray diffraction data through GSAS-II behind MCP and A2A, where a blank-harness construction run progresses through a lithium lanthanum zirconium oxide (LLZO) signal-to-noise ladder, reaches the 4 hour scan as a frontier case, and exposes the workflow-scope limits that remain. The same rubric that rewards credible fits also scores trajectory scope, making the frontier a contract failure rather than a pattern-fitting failure. As base models evolve, re-running AgentBuild is a re-tune, not a rebuild, and the scientist's authored contract remains the durable asset.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。