Update croissant.json

croissant.json

@@ -624,7 +624,7 @@
     "process-understanding",
     "manipulation"
   ],
-  "license": "https://choosealicense.com/licenses/other/",
+  "license": "https://creativecommons.org/licenses/by/4.0/",
   "url": "https://huggingface.co/datasets/ProcessBench-2026/RoboProcessBench",
   "rai:dataLimitations": "RoboProcessBench evaluates VLM-side process understanding in robotic manipulation, not closed-loop robot control or robot safety. It is derived from GM-100, RH20T, REASSEMBLE, and AIST-Bimanual, so source coverage, viewpoints, embodiments, sensing modalities, and task-family support are heterogeneous. Not every source supports every task family, and primitive-aware tasks depend on available primitive annotations. The benchmark uses multiple-choice QA and should be interpreted with task-level scores, random/majority baselines, human audit, and bootstrap CIs. It is not recommended for robot safety certification, deployment readiness claims, generic VLM ranking, or causal claims about downstream VLA policy improvement without additional validation.",
   "rai:dataBiases": "RoboProcessBench inherits biases from its four upstream robotic datasets, including robot embodiment, camera viewpoint, tabletop scene layout, object category, task-family coverage, sensing availability, annotation granularity, and success/failure distribution. Some process cues are available only for sources with suitable native signals or annotations, so task-family coverage is uneven. Labels are generated by deterministic builders from source-native signals, and label quality depends on upstream signal resolution, annotation quality, and calibration choices. Models may learn source-specific visual or embodiment priors if results are not interpreted by task and source.",