README: VR branding and Mark7121983123 paths

README.md

@@ -7,7 +7,7 @@ language:
   - en
 size_categories:
   - 100K<n<1M
-pretty_name: VBVR-MultiStep
+pretty_name: VR-MultiStep
 tags:
   - video-reasoning
   - multi-step
@@ -16,11 +16,11 @@ tags:
   - training
 ---
 
-# VBVR-MultiStep
+# VR-MultiStep
 
-The **~360k-sample programmatic training corpus** for long-horizon multi-step image-to-video (I2V) reasoning. Companion to the frozen [VBVR-MultiStep-Bench](https://huggingface.co/datasets/Video-Reason/VBVR-MultiStep-Bench) (180-instance evaluation split).
+The **~360k-sample programmatic training corpus** for long-horizon multi-step image-to-video (I2V) reasoning. Companion to the frozen **VR-MultiStep-Bench** (180-instance evaluation split).
 
-Part of the **VBVR (Very Big Video Reasoning Suite)** project: <https://video-reason.com>. See [Wang et al., ICML 2026](https://icml.cc/virtual/2026/poster/65709) for the parent suite.
+Context for the broader research line: see [Wang et al., ICML 2026](https://icml.cc/virtual/2026/poster/65709) and the project site [video-reason.com](https://video-reason.com).
 
 ## At a glance
 
@@ -55,10 +55,10 @@ Part of the **VBVR (Very Big Video Reasoning Suite)** project: <https://video-re
 The `sample/` subdirectory is a 5 GB pre-curated subset (the first 300 samples of every task) for reviewers and quick experimentation. To pull it:
 
 ```bash
-huggingface-cli download Video-Reason/VBVR-MultiStep \
+huggingface-cli download Mark7121983123/VR-MultiStep \
     --repo-type dataset \
     --include "sample/**" \
-    --local-dir ./vbvr-multistep-sample
+    --local-dir ./vr-multistep-sample
 ```
 
 ## Sample format (inside each `.tar.gz` shard)
@@ -91,7 +91,7 @@ tar xzf Multi-01_maze_shortest_path_data-generator_00000-00049.tar.gz
 ```python
 import pandas as pd
 m = pd.read_parquet(
-    "hf://datasets/Video-Reason/VBVR-MultiStep/data/metadata_shards/Multi-01_maze_shortest_path_data-generator.parquet"
+    "hf://datasets/Mark7121983123/VR-MultiStep/data/metadata_shards/Multi-01_maze_shortest_path_data-generator.parquet"
 )
 print(m.head())
 ```
@@ -102,7 +102,7 @@ print(m.head())
 from huggingface_hub import hf_hub_download
 import tarfile
 shard = hf_hub_download(
-    "Video-Reason/VBVR-MultiStep",
+    "Mark7121983123/VR-MultiStep",
     "questions/Multi-01_maze_shortest_path_data-generator_00000-00049.tar.gz",
     repo_type="dataset",
 )
@@ -113,10 +113,10 @@ with tarfile.open(shard) as t:
 ### Pull only the 5 GB sample
 
 ```bash
-huggingface-cli download Video-Reason/VBVR-MultiStep \
+huggingface-cli download Mark7121983123/VR-MultiStep \
     --repo-type dataset \
     --include "sample/**" \
-    --local-dir ./vbvr-multistep-sample
+    --local-dir ./vr-multistep-sample
 ```
 
 ## Splits and seeds
@@ -128,11 +128,11 @@ The training corpus is partitioned into disjoint seed bands:
 | First-half | 1–5,000 | 5,000 | ~170k (across 34 trained tasks) |
 | Second-half | 5,001–10,000 | 5,000 | ~170k (across 34 trained tasks) |
 
-Both bands are disjoint from the **180-instance evaluation seeds** in `VBVR-MultiStep-Bench`. The submitted paper trains on 34 of 36 tasks; the released corpus contains all 36 task families.
+Both bands are disjoint from the **180-instance evaluation seeds** in **VR-MultiStep-Bench**. The submitted paper trains on 34 of 36 tasks; the released corpus contains all 36 task families.
 
 ## Reasoning families
 
-See the [bench dataset card](https://huggingface.co/datasets/Video-Reason/VBVR-MultiStep-Bench) for the family taxonomy. Each family contributes 6 tasks, for 36 total.
+See the [evaluation split dataset card](https://huggingface.co/datasets/Mark7121983123/VR-MultiStep-Bench) for the family taxonomy. Each family contributes 6 tasks, for 36 total.
 
 ## Intended use and out-of-scope
 
@@ -149,4 +149,3 @@ Derivatives of `Wan2.2-I2V-A14B` (Apache-2.0) referenced in the companion paper
 ## Responsible AI
 
 The dataset is fully synthetic. There are no human subjects, no scraped media, and no personal or sensitive information. Known biases inherit from the deterministic generators — every task family covers a deliberately narrow conceptual slice, and visual style is controlled by a fixed renderer family (no demographic content). See [`croissant.json`](./croissant.json) for the complete RAI metadata.
-