物理作为衡量和纠正多模态模型中材料推理的标签
Physics as the label for measuring and correcting materials reasoning in multimodal models
AI总结：
本文提出无标签基准MatPCR，利用物理定律和材料数据验证多模态模型推理链的物理一致性，并引入约束接地自验证循环以纠正推理错误。
AI中文摘要：
视觉-语言模型和语言模型越来越多地解释材料数据，然而基准测试报告它们会幻觉出无效的属性并违反物理定律。评估将最终答案与稀缺的人工标签进行匹配，而发现智能体则验证最终提案或密度泛函理论（DFT）的执行。两者都没有衡量模型推理链的物理一致性。材料数据本身携带其自身的物理信息，使得一大类材料推理无需标注即可验证。我们引入了MatPCR，一个无标签基准，其程序化预言机通过布拉格定律检查衍射几何、比例尺、光谱峰，以及基于材料项目的近凸包稳定性、计算带隙类别和净磁化强度检查。我们定义了图像和结构输入上的物理一致性率；引入了约束接地自验证，一种智能体循环，其增益在自我精炼和等计算重新提示控制下得以保持；发布了一个在分布内有用但在所有六种保留约束类型上接近随机的开放验证器；并推导了一个关于预言机误差如何影响报告率的精确恒等式。
英文摘要：
Vision-language and language models increasingly interpret materials data, yet benchmarks report that they hallucinate invalid properties and violate physical law. Evaluation matches final answers to scarce human labels, while discovery agents verify final proposals or density functional theory (DFT) execution. Neither measures the physical consistency of a model's reasoning chain. Materials data carries its own physics, making a large class of materials reasoning verifiable without annotation. We introduce MatPCR, a label-free benchmark whose programmatic oracles check diffraction geometry through Bragg's law, scale bars, spectral peaks, and Materials Project-grounded checks of near-hull stability, computed band-gap class, and net magnetization. We define the Physical-Consistency Rate over image and structure inputs; introduce Constraint-Grounded Self-Verification, an agentic loop whose gain survives self-refinement and equal-compute re-prompting controls; release an open verifier useful in distribution but near chance on all six held-out constraint types; and derive an exact identity for how oracle error displaces the reported rate.
