Update README.md

README.md

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 ---
-license: other
+license: cc-by-4.0
 language:
 - en
 pretty_name: RoboProcessBench
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 # RoboProcessBench
 
+This benchmark is one core contribution of the paper, **"Robo2Process: Benchmarking and Learning Process-Aware Understanding in Vision-Language Robotic Manipulation"**, which has been accpeted at the 40th Conference on Neural Information Processing Systems (NeurIPS 2026). 
+
 ## Dataset Summary
 
-RoboProcessBench is a process-aware benchmark for vision-language robotic manipulation understanding. It evaluates whether VLMs can infer how a manipulation execution unfolds, including phase, contact, motion, bimanual coordination, primitive-local progress, temporal order, outcome, and primitive-level transitions.
+RoboProcessBench is a process-aware understanding benchmark for vision-language robotic manipulation. It evaluates whether VLMs can infer how a manipulation execution unfolds, including phase, contact, motion, bimanual coordination, primitive-local progress, temporal order, outcome, and primitive-level transitions.
 
 This release contains 57,892 QA rows: 48,841 SFT rows and 9,051 evaluation rows across 12 task families and 260 manipulation tasks. The split follows strict episode / recording / scene isolation. The benchmark is derived from GM-100, RH20T, REASSEMBLE, and AIST-Bimanual.
 
@@ -94,8 +96,17 @@ RoboProcessBench/
 Full upstream videos and full frame dumps are not redistributed in this release.
 
 
-## License and Terms
+## License and Attribution
+
+The original benchmark contributions in RoboProcessBench, including newly authored questions, answer annotations, benchmark organization, split manifests, and metadata, are released under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. This license applies only to materials for which the RoboProcessBench authors hold the necessary rights.
+
+Meanwhile, RoboProcessBench is constructed using four upstream robotic manipulation datasets:
+
+- GM-100
+- RH20T
+- REASSEMBLE
+- AIST-Bimanual
 
-This release uses `license: other`.
+The original datasets, including their videos, robot trajectories, sensor measurements, and other third-party materials, remain subject to their respective licenses and terms of use. The CC BY 4.0 license does not replace, override, or sublicense any upstream dataset license.
 
-- Derived benchmark metadata in this release remains subject to upstream dataset terms.
+Users who obtain or reconstruct visual observations from the original datasets must comply with the applicable upstream licenses and attribution requirements. See `metadata/asset_licenses.csv` for source-specific licensing information.