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README.md

@@ -73,3 +73,65 @@ configs:
   - split: test_ood
     path: data/test_ood-*
 ---
+
+# ToR-OT v2 synthetic optimal-transport corpus
+
+Discrete Kantorovich instances with exact sparse optimal plans, generated by
+`python -m torot.data.generate` from the ToR-OT code release
+("Learning Optimal Transport Plans Via Autoregressive Token Regression", NeurIPS 2026).
+
+Each record is one instance:
+
+| field | description |
+|---|---|
+| `n`, `m` | number of source / target bins |
+| `dim` | support dimension (1) |
+| `cost_type` | one of `sqeuclidean`, `euclidean`, `cityblock`, `chebyshev`, `minkowski`, `canberra` |
+| `coords_src`, `coords_tgt` | bin centres of a regular grid on [0,1], canonical order |
+| `a`, `b` | marginals (float64, sum to 1); uniformly random weights for the random splits |
+| `plan_rows`, `plan_cols`, `plan_vals` | COO of the optimal plan returned by POT's network simplex (`ot.emd`) |
+| `ot_cost` | optimal cost with the max-normalised cost matrix |
+| `family_src`, `family_tgt` | `uniform_random`, or the named density for the OOD evaluation split |
+| `seed` | per-instance seed (instances are fully reproducible) |
+
+The cost matrix is not stored; it is `torot.data.cost.cost_matrix(coords_src, coords_tgt, cost_type)`.
+
+## Splits
+
+| split | instances |
+|---|---|
+| `train` | 2000000 |
+| `val` | 5000 |
+| `test_id` | 10000 |
+| `test_extrap` | 5000 |
+| `test_ood` | 1728 |
+
+## Generation configuration
+
+```json
+{
+  "n_min": 5,
+  "n_max": 100,
+  "extrap_n_min": 120,
+  "extrap_n_max": 200,
+  "equal_sizes": true,
+  "cost_types": [
+    "sqeuclidean",
+    "euclidean",
+    "cityblock",
+    "chebyshev",
+    "minkowski",
+    "canberra"
+  ],
+  "ood_sizes": [
+    30,
+    100
+  ]
+}
+```
+
+Command:
+
+```
+python -m torot.data.generate --out_dir ./otdata_v2 --train 2000000 --val 5000 --test_id 10000 --test_extrap 5000 --seed 0 --workers 12 --repo_id ivanjaenm/torot_data_2M --push
+```