Yanyan Zhang

This paper introduces NEBULA, a unified ecosystem for evaluating Vision-Language-Action (VLA) agents beyond coarse end-task success metrics. It proposes a novel dual-axis evaluation framework that combines fine-grained capability tests for skill-specific diagnosis with systematic stress tests to measure robustness under real-world perturbations. In addition, NEBULA standardizes fragmented embodied AI datasets through a unified data format and API, enabling reproducible cross-dataset training and benchmarking. Experimental results reveal that state-of-the-art VLA models exhibit significant hidden weaknesses in critical capabilities such as spatial reasoning and dynamic adaptation, highlighting the need for more interpretable and reliability-aware evaluation. [oai_citation:0‡ICLR_2026_Nebula_Final.pdf](sediment://file_000000002e8c722f8ce2ecef4cc5af26)