为大型科学设施设计自主实验评估标准，定义6级智能等级。
Benchmarking Autonomy in Scientific Experiments: A Hierarchical Taxonomy for Autonomous Large-Scale Facilities
- 提出针对科研设施的6级自主性分级体系，支持零样本部署。
- 关键转折点在第3级，决策从反馈转向语义数字孪生。
- 适合设施管理者、资助方和实验科学家评估自动化风险与智能水平。
从自动化数据采集迈向全自主发现，亟需统一的评估语言。尽管汽车领域采用SAE J3016标准，现有科研自主性分类仍基于所有者-操作者模式，与大型用户设施的运行刚性不兼容。本文提出面向科学实验自主性的基准评估体系（BASE Scale），包含6个层级（0-5级），专为这些独特约束设计。与传统模式不同，用户设施要求零样本部署，代理必须立即运行而无需长期训练。我们为每级定义具体技术要求，识别出推理屏障（第3级）为关键延迟阈值——此时决策从标量反馈转变为语义数字孪生。该层级将决策空间从空间探索扩展至时间门控，使代理能同步采集与瞬态物理事件的发生。通过建立这些操作定义，BASE Scale为设施主管、资助机构及束线科学家提供了标准化指标，用于评估风险、界定责任并量化实验流程的智能程度。
原文摘要 · Abstract (English)
The transition from automated data collection to fully autonomous discovery requires a shared vocabulary to benchmark progress. While the automotive industry relies on the SAE J3016 standard, current taxonomies for autonomous science presuppose an owner-operator model that is incompatible with the operational rigidities of Large-Scale User Facilities. Here, we propose the Benchmarking Autonomy in Scientific Experiments (BASE) Scale, a 6-level taxonomy (Levels 0-5) specifically adapted for these unique constraints. Unlike owner-operator models, User Facilities require zero-shot deployment where agents must operate immediately without extensive training periods. We define the specific technical requirements for each tier, identifying the Inference Barrier (Level 3) as the critical latency threshold where decisions shift from scalar feedback to semantic digital twins. Fundamentally, this level extends the decision manifold from spatial exploration to temporal gating, enabling the agent to synchronise acquisition with the onset of transient physical events. By establishing these operational definitions, the BASE Scale provides facility directors, funding bodies, and beamline scientists with a standardised metric to assess risk, define liability, and quantify the intelligence of experimental workflows.
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