Update README.md

README.md

@@ -2,7 +2,7 @@
 license: other
 language:
 - en
-pretty_name: ProcessBench-v1
+pretty_name: ProcessBench
 task_categories:
 - visual-question-answering
 tags:
@@ -27,22 +27,41 @@ configs:
 
 ## Dataset Summary
 
-ProcessBench is a process-aware benchmark for robotic manipulation understanding. This release build contains `57,892` QA rows: `9,051` Eval rows and `48,841` SFT rows across `12` task families. The split follows strict episode / recording / scene isolation. Upstream dataset sources include `GM-100`, `RH20T`, `REASSEMBLE`, and `AIST-Bimanual`.
+ProcessBench 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.
+
+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.
 
 ## Task Families
 
-- `T1`: Phase Recognition
-- `T2`: Contact Detection
-- `T3`: Motion Direction Prediction
-- `T4`: Bimanual Coordination State
-- `T5`: Primitive-local Progress
-- `T6`: Motion State Recognition
-- `T7`: Operation Outcome Prediction
-- `T8`: Temporal Ordering
-- `T9`: Temporal Priority Prediction
-- `T10`: Current Primitive Recognition
-- `T11`: Next Primitive Prediction
-- `T12`: Primitive Chain Restoration
+ProcessBench contains 12 diagnostic question families:
+
+- **T1 Phase Recognition:** identify the current coarse process phase.
+- **T2 Contact Detection:** determine whether task-relevant contact has occurred.
+- **T3 Motion Direction Prediction:** infer the dominant motion direction from short temporal context.
+- **T4 Bimanual Coordination State:** identify the current coordination state of two arms.
+- **T5 Primitive-local Progress:** estimate progress within the current local manipulation step.
+- **T6 Motion State Recognition:** distinguish actively moving from stationary states.
+- **T7 Operation Outcome Prediction:** predict eventual success or failure from partial execution evidence.
+- **T8 Temporal Ordering:** reconstruct the chronological order of shuffled observations.
+- **T9 Temporal Priority Prediction:** decide which of two observations occurred earlier.
+- **T10 Current Primitive Recognition:** identify the current low-level primitive.
+- **T11 Next Primitive Prediction:** infer the next primitive from local process context.
+- **T12 Primitive Chain Restoration:** restore a masked primitive in a local primitive chain.
+
+
+## Repository Contents
+
+This release is organized around a minimal reviewable benchmark package:
+
+- `splits/`: SFT and Eval QA entries.
+- `metadata/`: split statistics, task distribution, and license notes.
+- `examples/`: rendered representative task cards examples.
+- `ProcessData-SFT-Qwen/`: ProcessData-SFT-Qwen LoRA adapter weights and configurations.
+- `ProcessData-SFT-Qwen_results/`:  ProcessData-SFT-Qwen predictions and score summaries.
+- `benchmark_card.md`: benchmark-level documentation.
+- `croissant.json`: Croissant metadata with core and Responsible AI fields.
+
+Full upstream videos and full frame dumps are not redistributed in this release.
 
 ## SFT-Eval Split Reconstruction