AI在物理发现中越来越重要，但必须验证其可靠性才能信任新发现。
Are We Ready for AI-Driven Discovery? AI Verification Before the Next Fundamental Physics Breakthrough
- 建立验证框架，评估机器学习在粒子物理等领域的可靠性
- 指出机器学习受归纳偏置和样本量限制，无法突破实验约束
- 适合关注AI在基础物理中应用的科研人员与审稿人参考
机器学习已深度融入基础物理研究，加速从数据采集到推断、假设检验的全流程。随着系统自主性提升，确保其在发现声明中的可靠性变得至关重要。本文综述了VERaiPHY（物理领域鲁棒人工智能的验证与评估）倡议提出的评估框架，覆盖粒子物理、天体物理和宇宙学领域。我们明确了在统计发现流程中验证的必要性，并强调三大根本局限：归纳偏置不可避免，样本复杂度限制学习能力，实验条件制约发现可能。物理学家的角色正从实验设计者转变为评估者，其判断将科学严谨性嵌入AI系统。负责任地整合机器学习，需同时认识其变革潜力与内在边界。
原文摘要 · Abstract (English)
Machine learning (ML) has become integral to fundamental physics, accelerating statistical workflows from data acquisition through inference and hypothesis testing. As ML systems grow increasingly autonomous, ensuring their reliability for discovery claims becomes critical. This review synthesizes the VERaiPHY (Validation & Evaluation for Robust AI in PHYsics) initiative's frameworks for rigorous ML assessment across particle physics, astrophysics, and cosmology. We establish when verification is essential by contextualizing ML within the statistical discovery workflow. We emphasize fundamental limitations: inductive bias is unavoidable, sample complexity bounds learning, and experimental constraints limit discovery. We reflect on physicists' evolving role as both experimental designers and evaluators whose judgments encode scientific rigor into AI systems. Responsible integration requires understanding ML's transformative potential alongside its intrinsic boundaries.
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