Video generation models have significantly advanced embodied intelligence, unlocking new possibilities for generating diverse robot data that capture perception, reasoning, and action in the physical world. However, synthesizing high-quality videos that accurately reflect real-world robotic interactions remains challenging, and the lack of a standardized benchmark limits fair comparisons and progress. To address this gap, we introduce a comprehensive robotics benchmark, RBench, designed to evaluate robot-oriented video generation across five task domains and four distinct embodiments. It assesses both task-level correctness and visual fidelity through reproducible sub-metrics, including structural consistency, physical plausibility, and action completeness. Evaluation of 25 representative models highlights significant deficiencies in generating physically realistic robot behaviors. Furthermore, the benchmark achieves a Spearman correlation coefficient of 0.96 with human evaluations, validating its effectiveness. While RBench provides the necessary lens to identify these deficiencies, achieving physical realism requires moving beyond evaluation to address the critical shortage of high-quality training data. Driven by these insights, we introduce a refined four-stage data pipeline, resulting in RoVid-X, the largest open-source robotic dataset for video generation with 4 million annotated video clips, covering thousands of tasks and enriched with comprehensive physical property annotations. Collectively, this synergistic ecosystem of evaluation and data establishes a robust foundation for rigorous assessment and scalable training of video models, accelerating the evolution of embodied AI toward general intelligence.

视频生成模型具有显着先进的体现智能，解锁了生成各种机器人数据的新可能性，这些数据可以捕获物理世界中的感知、推理和动作。然而，合成准确反映现实世界机器人交互的高质量视频仍然具有挑战性，并且缺乏标准化基准限制了公平比较和进步。为了解决这一差距，我们引入了一个全面的机器人基准测试 RBench，旨在评估跨五个任务域和四个不同实施例的面向机器人的视频生成。它通过可重复的子指标评估任务级别的正确性和视觉保真度，包括结构一致性、物理合理性和动作完整性。对 25 个代表性模型的评估凸显了在生成物理真实机器人行为方面的重大缺陷。此外，该基准与人类评估的 Spearman 相关系数达到 0.96，验证了其有效性。虽然 RBench 提供了识别这些缺陷的必要视角，但实现物理真实感需要超越评估，以解决高质量训练数据的严重短缺问题。在这些见解的驱动下，我们引入了一个完善的四阶段数据管道，从而产生了 RoVid-X，这是最大的视频生成开源机器人数据集，包含 400 万个带注释的视频剪辑，涵盖数千个任务，并丰富了全面的物理属性注释。总的来说，这个评估和数据的协同生态系统为视频模型的严格评估和可扩展训练奠定了坚实的基础，加速了嵌入式人工智能向通用智能的发展。

A molecule's properties are fundamentally determined by its composition and structure encoded in its molecular graph. Thus, reasoning about molecular properties requires the ability to parse and understand the molecular graph. Large Language Models (LLMs) are increasingly applied to chemistry, tackling tasks such as molecular name conversion, captioning, text-guided generation, and property or reaction prediction. Most existing benchmarks emphasize general chemical knowledge, rely on literature or surrogate labels that risk leakage or bias, or reduce evaluation to multiple-choice questions. We introduce MolecularIQ, a molecular structure reasoning benchmark focused exclusively on symbolically verifiable tasks. MolecularIQ enables fine-grained evaluation of reasoning over molecular graphs and reveals capability patterns that localize model failures to specific tasks and molecular structures. This provides actionable insights into the strengths and limitations of current chemistry LLMs and guides the development of models that reason faithfully over molecular structure.

分子的性质从根本上是由其分子图中编码的组成和结构决定的。因此，推理分子特性需要解析和理解分子图的能力。大型语言模型 (LLM) 越来越多地应用于化学，处理分子名称转换、字幕、文本引导生成以及性质或反应预测等任务。大多数现有基准强调一般化学知识，依赖于存在泄漏或偏见风险的文献或替代标签，或减少对多项选择题的评估。我们推出 MolecularIQ，一种分子结构推理基准，专门专注于符号可验证的任务。 MolecularIQ 能够对分子图推理进行细粒度评估，并揭示将模型故障定位到特定任务和分子结构的能力模式。这为当前化学法学硕士的优势和局限性提供了可行的见解，并指导了忠实推理分子结构的模型的开发。