diff --git a/headroom/evals/datasets.py b/headroom/evals/datasets.py
index 0645035c8..5c94a8cfa 100644
--- a/headroom/evals/datasets.py
+++ b/headroom/evals/datasets.py
@@ -439,6 +439,61 @@ def load_longbench(
     return EvalSuite(name=f"LongBench_{task}", cases=cases)
 
 
+def load_multi_wiki_qa(
+    n: int = 100,
+    lang: str = "ja",
+) -> EvalSuite:
+    """Load multilingual SQuAD-style QA (alexandrainst/multi-wiki-qa).
+
+    Covers Chinese/Japanese/Korean (and many more) with verbatim-span answers
+    over full Wikipedia articles -- the only HF-loadable set with uniform
+    zh/ja/ko extractive QA, so it yields comparable cross-language compression
+    answer-retention numbers. License: CC-BY-NC-SA-4.0 (non-commercial).
+
+    Common lang configs: "zh-cn", "ja", "ko".
+
+    Args:
+        n: Number of samples to load
+        lang: multi-wiki-qa language config (e.g. "ja", "ko", "zh-cn")
+
+    Returns:
+        EvalSuite with multi-wiki-qa cases
+    """
+    _check_datasets_installed()
+    from datasets import load_dataset
+
+    try:
+        ds = load_dataset("alexandrainst/multi-wiki-qa", lang, split=f"train[:{n}]")
+    except Exception as e:
+        raise ValueError(f"Failed to load multi-wiki-qa '{lang}': {e}") from e
+
+    cases: list[EvalCase] = []
+    for i, item in enumerate(ds):
+        context = item.get("context", "")
+        question = item.get("question", "")
+        answers = item.get("answers") or {}
+        texts = answers.get("text") if isinstance(answers, dict) else None
+        ground_truth = texts[0] if texts else None
+        if not (context and question and ground_truth):
+            continue
+
+        cases.append(
+            EvalCase(
+                id=f"multi_wiki_qa_{lang}_{i}",
+                context=context,
+                query=question,
+                ground_truth=ground_truth,
+                metadata={
+                    "source": "multi-wiki-qa",
+                    "lang": lang,
+                    "title": item.get("title", ""),
+                },
+            )
+        )
+
+    return EvalSuite(name=f"multi_wiki_qa_{lang}", cases=cases)
+
+
 def load_narrativeqa(
     n: int = 100,
     split: str = "test",
@@ -1189,6 +1244,13 @@ DATASET_REGISTRY: dict[str, dict[str, Any]] = {
         "category": "long_context",
         "default_n": 100,
     },
+    # Multilingual
+    "multi_wiki_qa": {
+        "loader": load_multi_wiki_qa,
+        "description": "Multilingual (zh/ja/ko) Wikipedia QA with verbatim-span answers",
+        "category": "rag_multilingual",
+        "default_n": 100,
+    },
     # Tool Use
     "bfcl": {
         "loader": load_bfcl,
diff --git a/tests/test_evals_multilingual.py b/tests/test_evals_multilingual.py
new file mode 100644
index 000000000..9562001ea
--- /dev/null
+++ b/tests/test_evals_multilingual.py
@@ -0,0 +1,29 @@
+"""multi-wiki-qa multilingual loader registration.
+
+The eval framework's DATASET_REGISTRY only had English datasets, so the
+LLM-in-the-loop runner could not be pointed at Chinese/Japanese/Korean. This
+registers `alexandrainst/multi-wiki-qa` (verbatim-span answers over full
+Wikipedia articles, uniform zh/ja/ko). The live HF load is exercised in the
+PR's Real Behavior Proof, not here, to keep the test offline (mirrors the other
+dataset loaders).
+"""
+
+from headroom.evals.datasets import DATASET_REGISTRY, load_multi_wiki_qa
+
+
+def test_multi_wiki_qa_registered():
+    assert "multi_wiki_qa" in DATASET_REGISTRY
+    entry = DATASET_REGISTRY["multi_wiki_qa"]
+    assert entry["loader"] is load_multi_wiki_qa
+    assert entry["category"] == "rag_multilingual"
+    # same 4-key shape as every other registry entry
+    assert set(entry) == {"loader", "description", "category", "default_n"}
+
+
+def test_multi_wiki_qa_default_lang_is_callable():
+    # signature is (n, lang) like the other loaders; default lang is a real config
+    import inspect
+
+    sig = inspect.signature(load_multi_wiki_qa)
+    assert list(sig.parameters) == ["n", "lang"]
+    assert sig.parameters["lang"].default in {"ja", "ko", "zh-cn"}