Card: name the results that need the grand-only snapshot; link the pseudo-reference paper

README.md

@@ -193,6 +193,10 @@ grand = load_dataset("wmt/wmt26-mteval-data", "grand", split="evaluation")
   error spans can tell the two snapshots apart (Tables 4-7 and 16-36 of the findings paper), and
   Table 6, which needs no human annotation, already fits the deduplicated snapshot: its errors per
   prediction for the Qwen 3.6 baselines match the deduplicated predictions, not the original ones.
+  The organisers' code computes some results in the paper from the `pre_dedup_pre_enhy_fix`
+  snapshot instead: the "[no dedup]" rows of Table 9 and the organiser baselines converted for
+  Task 3 (Tables 10-13 and 65-85). To reproduce these, read that snapshot from `grand`; from `alt`,
+  six of the ten "[no dedup]" rows of Table 9 come out differently.
 - A run may cover only some items, and then has no rows for the others; `systems.n_items`, `lps`
   and `covers_all_priority` say what it covers.
 - `systems` holds allow-listed model-card answers only: a fixed list of fields was copied, and
@@ -627,7 +631,7 @@ The contrastive error span annotation protocol (cESA) of the human evaluation:
 }
 ```
 
-The pipeline that built the pseudo-references:
+The pipeline that built the pseudo-references (<https://arxiv.org/abs/2609.13611>):
 
 ```bibtex
 @inproceedings{kanojia2026blind,
@@ -635,7 +639,9 @@ The pipeline that built the pseudo-references:
   author = {Kanojia, Diptesh and Lo, Chi-kiu and Sindhujan, Archchana and Larkin, Samuel and Hanneman, Greg and Lavie, Alon},
   booktitle = {Proceedings of the Eleventh Conference on Machine Translation},
   year = {2026},
-  publisher = {Association for Computational Linguistics}
+  publisher = {Association for Computational Linguistics},
+  eprint = {2609.13611},
+  archiveprefix = {arXiv}
 }
 ```
 

build_manifest.json

@@ -621,7 +621,7 @@
       "rows": 73
     }
   },
-  "git_commit": "73747073fccf95b2f6f9872485d6e4538f069041",
+  "git_commit": "689ce244ff136f69c1130c6b73da6dab2cc05936",
   "git_dirty": false,
   "grand_sha256": "a61675552cebb68fb8303cf08a23fbec31e5bb4e0bd26a1fdee1a9bfafc2fb7a",
   "pyarrow_version": "23.0.1",