AI 中文总结
研究以机器人化学实验室为测试平台，使科学智能可衡量，通过4608次试验发现工作流程执行率低、长期规划有挑战，反馈促使局部调整，提供了部署评估及改进诊断框架。
AI 中文摘要
人工智能通过知识、推理和计划生成来评估，然而科学智能需要可靠的物理行动和对证据的适应能力。在此，我们使用机器人化学实验室作为物理世界测试平台，以使科学智能可衡量。其45个模块化工作站作为机器可读技能进行展示，共进行了4608次试验。在实验室限制下，只有3.3%的试验产生了专家评估的可执行工作流程；即使是最佳系统也仅达到28.1%。长期规划仍是挑战：只有三个可执行工作流程超过30个操作，最长的包含44个。经过五轮实验，反馈促使进行局部调整，但没有工作流程级的重新规划或分析方法的重新设计。通过使物理可执行性和基于证据的重新规划可衡量，我们的研究为部署准备情况提供了基于证据的评估，并为指导朝着物理基础的自主研究进行闭环改进提供了诊断框架。
英文摘要
AI is evaluated through knowledge, reasoning and plan generation, yet scientific agency requires reliable physical action and adaptation to evidence. Here, we use a robotic chemistry laboratory as a physical-world testbed to make scientific agency measurable. Its 45 modular workstations exposed as machine-readable skills enabled 4,608 trials. Only 3.3% of trials produced expert-assessed executable workflows under laboratory constraints; even the best system achieved 28.1%. Long-horizon planning remained a challenge: only three executable workflows exceeded 30 operations, although the longest contained 44. Across five rounds, experimental feedback prompted local adjustments but no workflow-level replanning or analytical-method redesign. By making physical executability and evidence-driven replanning measurable, our study provides an evidence-based assessment of deployment readiness and a diagnostic framework to guide closed-loop improvements towards physically grounded autonomous research.
