让思想回归机器学习核心，用可验证行为预测取代盲目刷榜。
Position: Ideas Should be the Center of Machine Learning Research
- 以思想为先，通过定制实验验证模型行为模式
- 摆脱复杂算力依赖，小团队也能做扎实研究
- 适合重视科学本质、追求机制理解的研究者
机器学习研究正分裂为两种脱节的范式：以指标为导向的工程实践和难以落地的理想化理论。本文主张，当前过度关注两端而忽视了核心科学对象——思想。提出“思想优先”框架：将思想的价值体现在其对现代模型行为特征的预测上，并设计针对性实验来检测这些特征，而非追求排行榜领先。这一转变不仅弥合理论与实践的鸿沟，还通过消除“复杂性溢价”促进公平，使计算、资金和人力资源有限的研究者也能做出严谨科学贡献。最终倡导以思想为中心的研究文化，将基准测试与定理视为检验机制假设的工具，而非终极目标。
原文摘要 · Abstract (English)
Machine learning research increasingly bifurcates into two disconnected modes: benchmark-driven engineering that prioritizes metrics over understanding, and idealized theory that often fails to transfer to modern systems. In this position paper, we argue that the field focuses too heavily on these endpoints, neglecting the central scientific object: the idea. We propose an Ideas First framework in which ideas are valued for the behavioral signatures they predict in modern models, and these signatures are tested through tailored experiments designed to detect the relevant patterns rather than to win leaderboards. This shift not only bridges the gap between theory and practice but also promotes equity by removing the "complexity premium," enabling rigorous scientific contributions from researchers with modest computational, financial, and human resources. Ultimately, we advocate for a research culture centered on ideas, treating benchmarks and theorems as instruments for testing mechanistic hypotheses rather than as ends in themselves.
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