OPERA：面向语言模型智能体的可靠自主光学实验的算子残差反馈
OPERA: Operator-residual feedback for reliable autonomous optical experiments with language-model agents
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中文总结 AI 辅助
本研究提出面向语言模型智能体的OPERA算子残差框架，将实验动作表示为光学算子并结合物理解释残差评估，在三项光学任务中可减少无物理改进的决策占比，提升任务目标达成概率并降低实验预算。
中文摘要 AI 辅助
自主智能体基于可能无法反映实验成功的分数选择动作。我们开发了OPERA，这是一种用于光学实验的算子残差框架。它将实验动作表示为光学算子，并使用物理解释的残差评估其结果。算子指定对测量、控制或重建的可执行更改，而残差报告与指定物理条件的偏离。智能体使用两者来选择、组合或生成算子，并且物理性能会针对保留的参考进行独立评估。在三项光学任务中，仅使用分数反馈的决策中有23.6%至39.0%出现分数增加但无物理改进，而使用算子残差反馈的该比例仅为0.9%至1.9%。算子残差反馈提高了达到和维持任务目标的概率，并减少了实验预算。在数字孪生中选择的方案被转移到三台光学仪器，重复实验显示结构化光重建中的投影预算更低。总之，算子和残差利用可测量的物理证据指导自主决策。
英文摘要
Autonomous agents choose actions using scores that may not reflect experimental success. We developed OPERA, an operator-residual framework for optical experiments. It represents experimental actions as optical operators and evaluates their outcomes using physically interpretable residuals. Operators specify executable changes to measurement, control or reconstruction, while residuals report departures from specified physical conditions. The agent uses both to select, combine or generate operators, and physical performance is evaluated independently against a withheld reference. Across three optical tasks, score-only feedback produced score increases without physical improvement in 23.6--39.0\% of decisions, compared with 0.9--1.9\% for operator-residual feedback. Operator-residual feedback increased the probability of reaching and maintaining task targets and reduced experimental budgets. Protocols selected in digital twins were transferred to three optical instruments, and repeated experiments showed a lower projection budget in structured-light reconstruction. Together, operators and residuals guide autonomous decisions using measurable physical evidence.
发表机构
- Tsinghua University（清华大学）
- ModelBest Inc.（模贝斯特公司）
- Hunan University（湖南大学）
- Northwest Institute of Nuclear Technology（西北核技术研究所）
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