Update README.md

README.md

@@ -1,3 +1,4 @@
+```markdown
 ---
 pretty_name: Matrix-Game 2.0 Self-Consistency Reproduction
 tags:
@@ -9,12 +10,13 @@ tags:
 
 # Matrix-Game 2.0 Self-Consistency Reproduction
 
-This repository reproduces the Matrix-Game 2.0 self-consistency (SC) row in
-*Reasoning-Structured Videos: A Stratified Diagnostic Suite for Compositional
-Consistency in World Models*.
+This README explains how to reproduce the Matrix-Game 2.0 self-consistency (SC) row in *Reasoning-Structured Videos: A Stratified Diagnostic Suite for Compositional Consistency in World Models*.
 
-It evaluates the released full-rollout model videos directly. Ground-truth videos
-are not needed for SC.
+All experimental data, generated videos, and evaluation files required for this reproduction are available on the current dataset page under **Files and versions**:
+
+<https://huggingface.co/datasets/VideoWorldmodel/Evaluation>
+
+No external dataset or ground-truth video is required for this reproduction.
 
 ## What is reproduced
 
@@ -22,122 +24,155 @@ The statistical unit is one relation graph:
 
 - **Inverse-SC:** generated first frame versus generated final frame.
 - **Loop-SC:** generated first frame versus generated final frame.
-- **Equivalence-SC:** generated final frame of branch A versus generated final
-  frame of branch B.
-
-The release contains 448 Inverse graphs, 445 Loop graphs, and 239 Equivalence A/B
-pairs. The script checks these counts and pairing integrity before evaluation.
-
-## Data
-
-Reproduction bundle (videos, evaluator, and this README):
-
-<https://huggingface.co/datasets/VideoWorldmodel/Evaluation>
-
-The generated videos were originally released at:
+- **Equivalence-SC:** generated final frame of branch A versus generated final frame of branch B.
 
-<https://huggingface.co/datasets/WhynotGPT/Nips_WM_Eval_qzf>
+The released data contains:
+- 448 Inverse graphs
+- 445 Loop graphs
+- 239 Equivalence A/B pairs
 
-The benchmark GT dataset is available separately, but is not required by this SC
-reproduction:
+Before evaluation, the script verifies the expected file counts and checks the integrity of the Equivalence A/B pairings.
 
-<https://huggingface.co/datasets/VideoWorldmodel/ReasoningStructureTestset>
+## Download the experimental data
 
-Download and extract the MP4-only reproduction archive:
+The MP4-only reproduction archive is available under **Files and versions** on the current dataset page.
 
+Install the Hugging Face CLI and download the archive:
 ```bash
 python -m pip install -U huggingface_hub
+
 hf download VideoWorldmodel/Evaluation \
   --repo-type dataset \
   --include "MatrixGame2_SC_videos.zip" \
   --local-dir .
-unzip MatrixGame2_SC_videos.zip -d data/Nips_WM_Eval_qzf
 ```
 
-The expected layout is:
+Extract the archive:
+```bash
+unzip MatrixGame2_SC_videos.zip -d data/matrix_game_sc
+```
 
-```text
-data/Nips_WM_Eval_qzf/
-|-- inverse_easy/      # 246 MP4s
-|-- inverse_hard/      # 202 MP4s
-|-- loop_easy/         # 247 MP4s
-|-- loop_hard/         # 198 MP4s
-`-- equivalence/       # 478 MP4s = 239 A/B pairs
+The expected directory layout is:
+```
+data/matrix_game_sc/
+|-- inverse_easy/      # 246 MP4 files
+|-- inverse_hard/      # 202 MP4 files
+|-- loop_easy/         # 247 MP4 files
+|-- loop_hard/         # 198 MP4 files
+`-- equivalence/       # 478 MP4 files = 239 A/B pairs
 ```
 
-If a Git LFS clone was used, ensure the files are real video objects rather than
-small pointer files before evaluation.
+If the dataset was cloned using Git LFS, verify that the downloaded files are real MP4 files rather than small Git LFS pointer files.
 
 ## Environment
 
-Python 3.10 or newer is recommended. For a standard CPU/default PyTorch install:
+Python 3.10 or newer is recommended.
 
+Create and activate a virtual environment (Linux/macOS):
 ```bash
 python -m venv .venv
-source .venv/bin/activate  # Windows: .venv\Scripts\activate
+source .venv/bin/activate
+```
+
+On Windows:
+```powershell
+.venv\Scripts\activate
+```
+
+Install the required dependencies:
+```bash
 python -m pip install --upgrade pip
-python -m pip install "numpy>=1.26,<3" "opencv-python-headless>=4.8,<5" \
-  "torch>=2.2,<3" "torchvision>=0.17,<1" "lpips==0.1.4"
+
+python -m pip install \
+  "numpy>=1.26,<3" \
+  "opencv-python-headless>=4.8,<5" \
+  "torch>=2.2,<3" \
+  "torchvision>=0.17,<1" \
+  "lpips==0.1.4"
 ```
 
-For a specific CUDA build, install the matching `torch` and `torchvision` wheels
-from the official PyTorch instructions first, then install NumPy, OpenCV, and
-`lpips==0.1.4` from the command above without reinstalling PyTorch.
+For a specific CUDA build, first install the matching torch and torchvision wheels by following the official PyTorch installation instructions. Then install NumPy, OpenCV, and lpips==0.1.4 without reinstalling PyTorch.
 
 The first LPIPS run downloads the official AlexNet weights.
 
-## Reproduce the paper row
+## Reproduce the paper results
 
+Run the complete evaluation:
 ```bash
 python evaluate_matrix_game_sc.py \
-  --data data/Nips_WM_Eval_qzf \
+  --data data/matrix_game_sc \
   --output results/matrix_game_sc \
   --lpips \
   --device auto \
   --check-paper
 ```
 
-Use `--device cuda`, `--device cuda:0`, or `--device cpu` to select a device
-explicitly. A fast PSNR-only audit can be run by omitting `--lpips` and
-`--check-paper`.
+Available device options include:
+- `--device auto`
+- `--device cpu`
+- `--device cuda`
+- `--device cuda:0`
 
-Expected results:
+Example: run the evaluation explicitly on CPU:
+```bash
+python evaluate_matrix_game_sc.py \
+  --data data/matrix_game_sc \
+  --output results/matrix_game_sc \
+  --lpips \
+  --device cpu \
+  --check-paper
+```
 
-| Relation | Graph N | Recomputed LPIPS / PSNR | Paper LPIPS / PSNR |
-| --- | ---: | ---: | ---: |
-| Inverse-SC | 448 | 0.7061 / 10.4476 | 0.71 / 10.45 |
-| Loop-SC | 445 | 0.7173 / 10.6227 | 0.72 / 10.62 |
-| Equivalence-SC | 239 | 0.5918 / 12.5732 | 0.59 / 12.57 |
+For a faster PSNR-only audit, omit `--lpips` and `--check-paper`:
+```bash
+python evaluate_matrix_game_sc.py \
+  --data data/matrix_game_sc \
+  --output results/matrix_game_sc_psnr \
+  --device auto
+```
+
+## Expected results
+
+| Relation       | Graph N | Recomputed LPIPS / PSNR | Paper LPIPS / PSNR |
+|----------------|---------|-------------------------|--------------------|
+| Inverse-SC     | 448     | 0.7061 / 10.4476        | 0.71 / 10.45       |
+| Loop-SC        | 445     | 0.7173 / 10.6227        | 0.72 / 10.62       |
+| Equivalence-SC | 239     | 0.5918 / 12.5732        | 0.59 / 12.57       |
+
+The `--check-paper` option succeeds when all six computed values reproduce the precision reported in the paper.
 
-`--check-paper` passes when all six values reproduce the precision reported in the
-paper. Two Equivalence pairs are exact pixel matches. Their PSNR is infinite, so
-they are reported in `psnr_exact_match_n` and excluded from the finite PSNR mean;
-this produces the reported Equivalence PSNR.
+Two Equivalence pairs are exact pixel matches. Their PSNR values are infinite, so they are recorded in `psnr_exact_match_n` and excluded from the finite PSNR mean. This produces the reported Equivalence PSNR.
 
 ## Outputs
 
 The output directory contains:
-
-```text
-per_graph.csv    # one row per Inverse/Loop graph or Equivalence A/B pair
-summary.csv      # means, standard deviations, and graph-bootstrap 95% CIs
-paper_check.csv  # computed values versus paper values at reported precision
-audit.json       # metric definitions, counts, versions, and bootstrap settings
-report.md        # compact Markdown result table
-```
+- `per_graph.csv`: One row per Inverse/Loop graph or Equivalence A/B pair
+- `summary.csv`: Means, standard deviations, and graph-bootstrap 95% CIs
+- `paper_check.csv`: Computed values compared with the reported paper values
+- `audit.json`: Metric definitions, counts, versions, and bootstrap settings
+- `report.md`: Compact Markdown results table
 
 Confidence intervals use 10,000 graph-level bootstrap resamples with seed 2026.
-LPIPS uses `lpips==0.1.4`, AlexNet, and RGB inputs scaled to `[-1, 1]`. PSNR is
-computed on decoded RGB uint8 endpoints with `MAX=255`; no resizing is applied.
+
+### Metric settings
+- LPIPS uses `lpips==0.1.4` with AlexNet.
+- LPIPS inputs are RGB images scaled to [-1, 1].
+- PSNR is computed on decoded RGB uint8 endpoint frames.
+- PSNR uses MAX=255.
+- No resizing is applied.
+- Exact pixel matches are reported separately from the finite PSNR mean.
 
 ## Tests
 
+Run the test suite with:
 ```bash
 python -m unittest discover -s tests -v
 ```
 
 ## Scope
 
-This code reproduces the Matrix-Game 2.0 **SC endpoint metrics only**. It does not
-claim to reproduce the paper's GT-anchored metrics or FVD, which require additional
-generation-context and clip-sampling details.
+This code reproduces the Matrix-Game 2.0 SC endpoint metrics only.
+It does not claim to reproduce the paper’s GT-anchored metrics or FVD, which require additional generation-context and clip-sampling details.
+
+All data required for the SC endpoint reproduction described in this README is available on the current dataset page.
+```