Align the card with the EMNLP 2026 camera-ready (entropy over L-1 slots, candidate formula, at-most-five probes, grid provenance, runs not seeds, author order)

README.md

@@ -98,9 +98,11 @@ Built from the HuggingFace `wmt19` corpus with `ladit/data/prepare_mt_data*.py`:
 - **En↔Zh** — `load_dataset("wmt19", "zh-en", split="train")`. Filter: `5 <= len(en.split()) <= 200` and `2 <= len(zh) <= 600` (English whitespace words, Chinese characters).
 - **En→De** — `load_dataset("wmt19", "de-en", split="train")`, shuffled with `seed=42` before iteration. Filter: `5 <= len(en.split()) <= 200` and `5 <= len(de.split()) <= 200`.
 
-After filtering, pairs are shuffled with `random.seed(42)`; the first 200,000 become the train split and the next 2,000 become the dev split.
+After filtering, pairs are shuffled deterministically (`random.seed(42)` in the released scripts); the first 200,000 become the train split and the next 2,000 become the dev split.
 
-**Only single-side length filters are applied.** No language-ID classifier and no source–target length-ratio filter, so the corpus length statistics that set the EV candidate grid are not themselves the product of a ratio filter.
+**Only single-side length filters are applied.** No language-ID classifier and no source–target length-ratio filter.
+
+**What the dev split is *not* used for.** The WMT19 development pairs are not used to set the reported EV ratio grids or any decoding hyperparameter. The grids reported in the paper are direction-specific and were informed by diagnostic range comparisons on WMT22 subsets; the WMT19 training-corpus medians (0.80 / 1.24 / 1.48) serve only as a reference scale. Once chosen, a grid is fixed and used for every sentence and every length method in that direction.
 
 ### Test sets
 
@@ -155,6 +157,8 @@ python -m ladit.data.build_challenge_sets --input data/wmt22_enzh_test.jsonl \
 
 ## Licensing and Provenance
 
+The paper's *Resource availability* paragraph (Appendix A) states that the processed experiment datasets and trained LoRA adapters are released here; this repository is that release.
+
 These files are **derived subsets** of the public WMT19 training corpora and the public WMT22 News Translation test sets, redistributed here in a reformatted JSONL layout so that the paper's results are reproducible without re-running the sampling pipeline. They are **not** a new corpus, and no raw WMT distribution is mirrored in full.
 
 Use is subject to the original WMT terms, which permit use for machine-translation research. Source references:
@@ -171,7 +175,7 @@ If you are a rights holder for any WMT19 sub-corpus and want a portion removed,
 ```bibtex
 @inproceedings{zhan2026lengthadaptive,
   title     = {Length-Adaptive Decoding for Masked Diffusion Machine Translation},
-  author    = {Zhan, Yan and Zhang, Wanting and Hou, Mengkai and Gao, Zhijun},
+  author    = {Zhan, Yan and Hou, Mengkai and Zhang, Wanting and Gao, Zhijun},
   booktitle = {Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
   year      = {2026}
 }