Reliable benchmarking is critical for advancing Vision-Language-Action (VLA) models, as it reveals their generalization, robustness, and alignment of perception with language-driven manipulation tasks. However, existing benchmarks often provide limited or misleading assessments due to insufficient evaluation protocols that inadequately capture real-world distribution shifts. This work systematically rethinks VLA benchmarking from both evaluation and data perspectives, introducing LIBERO-X, a benchmark featuring: 1) A hierarchical evaluation protocol with progressive difficulty levels targeting three core capabilities: spatial generalization, object recognition, and task instruction understanding. This design enables fine-grained analysis of performance degradation under increasing environmental and task complexity; 2) A high-diversity training dataset collected via human teleoperation, where each scene supports multiple fine-grained manipulation objectives to bridge the train-evaluation distribution gap. Experiments with representative VLA models reveal significant performance drops under cumulative perturbations, exposing persistent limitations in scene comprehension and instruction grounding. By integrating hierarchical evaluation with diverse training data, LIBERO-X offers a more reliable foundation for assessing and advancing VLA development.
LIBERO-PRO: Towards Robust and Fair Evaluation of Vision-Language-Action Models Beyond Memorization
Huazhong University of Science and Technology（华中科技大学）；College of AI, Tsinghua University（清华大学人工智能学院）；Wuhan University of Technology（武汉理工大学）；Lehigh University（莱斯大学）
LIBERO-Safety: A Comprehensive Benchmark for Physical and Semantic Safety in Vision-Language-Action Models
Institute for AI Industry Research (AIR), Tsinghua University（清华大学人工智能产业研究院）；Beijing Academy of Artificial Intelligence (BAAI)（北京智源人工智能研究院）；Beihang University（北京航空航天大学）；Eastern Institute of Technology, Ningbo（宁波东方理工大学）；Shanghai Jiao Tong University（上海交通大学）；Microsoft Research Asia (MSRA)（微软亚洲研究院）
CAC-VLA: Context-Gated Action Conditioning for Vision-Language-Action Models
University of Science and Technology of China (USTC)（中国科学技术大学）；Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（合肥综合性国家科学中心人工智能研究院）
VLAFlow: A Unified Training Framework for Vision-Language-Action Models via Co-training and Future Latent Alignment
Li Auto Inc.（理想汽车）；School of Artificial Intelligence, Beijing University of Posts and Telecommunications（北京邮电大学人工智能学院）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））
LIBERO-Industry: Benchmarking Robotic Manipulation under Industrial Process Constraints
Institute of AI for Engineering, Tongji University（同济大学工程人工智能研究院）；The Chinese University of Hong Kong（香港中文大学）；University of Aberdeen（阿伯丁大学）；University of Cambridge（剑桥大学）
OGPO: Offline Goal-conditioned Policy Optimization for Recoverable Vision-Language-Action Models
Institute of Software, Chinese Academy of Sciences（中国科学院软件研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Wangxuan Institute of Computer Technology, Peking University（北京大学王选计算机技术研究所）
VLA-JEPA: Enhancing Vision-Language-Action Model with Latent World Model
University of Science and Technology of China（中国科学技术大学）；Zhongguancun Academy, Beijing, China（中关村学院）；Shanghai Jiao Tong University（上海交通大学）；Tsinghua University（清华大学）；Eastern Institute of Technology, Ningbo（宁波东部科技研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Nankai University（南开大学）
Hermite Curves as Trajectory Priors for Vision-Language-Action Models
School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳）计算机科学与技术学院）；Jiangsu Cytoderm Intelligent Technology Co., Ltd.（江苏赛克德智能科技有限公司）；National University of Singapore（新加坡国立大学）
MixVLA: Adaptive Mixing of Non-Invariant Information for Generalizable Vision-Language-Action Models
Fudan University（复旦大学）；Shanghai AI Laboratory（上海人工智能实验室）；Harbin Institute of Technology, Shenzhen, China（哈尔滨工业大学（深圳））；Northwestern Polytechnical University（西北工业大学）；TeleAI, China Telecom Corp Ltd（中国电信股份有限公司TeleAI）
See, Plan, Rewind: Progress-Aware Vision-Language-Action Models for Robust Robotic Manipulation
School of Information Science and Technology, University of Science and Technology of China（信息科学与技术学院，中国科学技术大学）；University of Technology Sydney（新南威尔士大学）；Department of Computer Vision, Mohamed Bin Zayed University of Artificial Intelligence（人工智能与计算机视觉系，Mohamed Bin Zayed人工智能大学）；The University of Hong Kong（香港大学）；Institute of AI for Industry, Chinese Academy of Sciences（产业人工智能研究所，中国科学院）；School of Intelligent Science and Engineering, Harbin Institute of Technology (Shenzhen)（智能科学与工程学院，哈尔滨工业大学（深圳））
G$^3$VLA: Geometric Inductive Bias for Vision-Language-Action Models
New York University Shanghai（上海纽约大学）；Technical University of Denmark（丹麦技术大学）；MBZUAI - Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；New York University Abu Dhabi（纽约大学阿布扎比分校）
