diff --git a/src/kb/eval/run_retrieval_only.py b/src/kb/eval/run_retrieval_only.py
index 0fa2f00..53b07ee 100644
--- a/src/kb/eval/run_retrieval_only.py
+++ b/src/kb/eval/run_retrieval_only.py
@@ -153,7 +153,7 @@ async def _run(args: argparse.Namespace) -> int:
                 question,
                 top_k_dense=top_k_dense,
                 top_k_sparse=top_k_sparse,
-                rerank_top_k=top_k_dense,
+                rerank_top_k=rerank_top_k,
                 filters={"project": args.project},
             )
             if hits:
diff --git a/src/kb/query/engine.py b/src/kb/query/engine.py
index f2ee395..7c71f48 100644
--- a/src/kb/query/engine.py
+++ b/src/kb/query/engine.py
@@ -160,6 +160,33 @@ def _format_sources(hits: list[dict[str, Any]]) -> str:
     return "\n\n".join(out)
 
 
+def _combine_sources_for_verification(
+    graph_sources: list[dict[str, Any]], retrieval_sources: list[dict[str, Any]]
+) -> list[dict[str, Any]]:
+    """Flatten graph and retrieval sources in the same order as prompt numbering."""
+    return [*graph_sources, *retrieval_sources]
+
+
+def _verification_sources(
+    graph_result: dict[str, Any] | None, serializable_hits: list[dict[str, Any]]
+) -> list[dict[str, Any]]:
+    """Build verifier inputs in the same order as the prompt source list."""
+    graph_sources: list[dict[str, Any]] = []
+    if graph_result and graph_result.get("mentions"):
+        for m in graph_result["mentions"]:
+            graph_sources.append(
+                {
+                    "text": m.get("excerpt", ""),
+                    "metadata": {
+                        "file_id": m.get("file_id", ""),
+                        "page_start": m.get("page_start", 0),
+                        "page_end": m.get("page_end", m.get("page_start", 0)),
+                    },
+                }
+            )
+    return _combine_sources_for_verification(graph_sources, serializable_hits)
+
+
 def _bookend_reorder(hits: list[Any]) -> list[Any]:
     """Reorder so highest-relevance chunks sit at the START and END of the prompt.
 
@@ -788,7 +815,7 @@ def _section_score(h: Any) -> float:
         started = time.time()
         checks = await verify_citations(
             answer=answer_text,
-            sources=serializable_hits,
+            sources=_verification_sources(graph_result, serializable_hits),
             model=pipeline.get(cfg, "llm.synthesize.model"),
         )
         verify_summary = verification_summary(checks)
diff --git a/tests/test_eval_retrieval_only.py b/tests/test_eval_retrieval_only.py
new file mode 100644
index 0000000..2f72657
--- /dev/null
+++ b/tests/test_eval_retrieval_only.py
@@ -0,0 +1,77 @@
+"""Retrieval-only eval wiring."""
+
+from __future__ import annotations
+
+import asyncio
+from argparse import Namespace
+
+import yaml
+
+from kb.eval import run_retrieval_only as eval_mod
+from kb.vector.base import SearchHit
+
+
+class FakeStore:
+    def __init__(self) -> None:
+        self.calls: list[dict] = []
+
+    async def ensure_collection(self, domain: str) -> None:
+        return None
+
+    async def hybrid_search(self, domain: str, query: str, **kwargs):
+        self.calls.append({"domain": domain, "query": query, **kwargs})
+        return [
+            SearchHit(
+                id="hit-1",
+                text="alpha beta",
+                score=1.0,
+                metadata={"file_id": "file-1", "page_start": 1, "page_end": 1},
+            )
+        ]
+
+
+def test_retrieval_only_uses_configured_rerank_top_k(monkeypatch, tmp_path) -> None:
+    ds_path = tmp_path / "ds.yaml"
+    ds_path.write_text(
+        yaml.safe_dump(
+            {
+                "questions": [
+                    {"id": "q1", "question": "alpha", "expected_files": ["file-1"], "tags": []}
+                ]
+            }
+        )
+    )
+    out_path = tmp_path / "out.json"
+
+    store = FakeStore()
+
+    monkeypatch.setattr(eval_mod, "get_store", lambda: store)
+    monkeypatch.setattr(
+        eval_mod.pipeline,
+        "pipeline_config",
+        lambda domain: {"retrieve": {"top_k_dense": 15, "top_k_sparse": 9, "rerank_top_k": 4}},
+    )
+
+    async def fake_get_file(file_id: str):
+        return {"id": file_id, "filename": f"{file_id}.pdf"}
+
+    monkeypatch.setattr(eval_mod.repo, "get_file", fake_get_file)
+
+    async def fake_rerank(query: str, hits, top_k: int):
+        return hits[:top_k]
+
+    monkeypatch.setattr(eval_mod, "cross_rerank", fake_rerank)
+
+    rc = asyncio.run(
+        eval_mod._run(
+            Namespace(
+                project="default",
+                domain="sec",
+                dataset=str(ds_path),
+                output=str(out_path),
+            )
+        )
+    )
+
+    assert rc == 0
+    assert store.calls[0]["rerank_top_k"] == 4
diff --git a/tests/test_query_synthesis.py b/tests/test_query_synthesis.py
index 72ba858..d9f37bd 100644
--- a/tests/test_query_synthesis.py
+++ b/tests/test_query_synthesis.py
@@ -11,6 +11,7 @@
     _extract_cited_indices,
     _format_numbered_sources,
     _format_sources,
+    _verification_sources,
 )
 from kb.query.intent import QueryIntent
 from kb.query.structured import _safe_field_key, maybe_structured_answer
@@ -110,6 +111,29 @@ async def fake_mentions_for(entity_ids: list[str], project: str = "default"):
     assert out["entities"][0]["id"] == "e1"
 
 
+def test_verification_sources_include_graph_then_retrieval() -> None:
+    out = _verification_sources(
+        {
+            "mentions": [
+                {
+                    "file_id": "graph-1",
+                    "page_start": 1,
+                    "page_end": 2,
+                    "excerpt": "graph evidence",
+                }
+            ]
+        },
+        [
+            {
+                "text": "retrieval evidence",
+                "metadata": {"file_id": "retr-1", "page_start": 3, "page_end": 3},
+            }
+        ],
+    )
+
+    assert [s["metadata"]["file_id"] for s in out] == ["graph-1", "retr-1"]
+
+
 def test_trailing_confidence_json_pattern() -> None:
     sample = 'The answer is X. [1] {"confidence": 0.83, "confidence_reason": "well-supported"}'
     m = re.search(r"\{[^{}]*confidence[^{}]*\}\s*$", sample)