diff --git a/codex/cli.py b/codex/cli.py
index 0816d6e..33ab2c8 100644
--- a/codex/cli.py
+++ b/codex/cli.py
@@ -103,7 +103,8 @@ def search_paper(
             results = []
             for row in rows:
                 pr_score = pr.get(row["id"], 0.0)
-                boosted = row["distance"] * (1.0 + alpha * pr_score)
+                # distance is lower=better; divide to reduce distance for high-PR papers
+                boosted = row["distance"] / (1.0 + alpha * pr_score)
                 results.append((boosted, row))
             results.sort(key=lambda x: x[0])
             for boosted_dist, row in results:
@@ -535,20 +536,23 @@ def graph_report(
     output_json: bool = typer.Option(False, "--json", help="Output as JSON."),
 ) -> None:
     """Show citation graph report: top hub papers, dangling citations, cluster summary."""
+    from codex.config import get_settings
     from codex.db import get_conn
     from codex.graph import build_citation_graph, citation_pagerank, dangling_citations
 
+    settings = get_settings()
     with get_conn() as conn:
         graph = build_citation_graph(conn)
         known_ids = {row["id"] for row in conn.execute("SELECT id FROM papers").fetchall()}
 
     if graph.number_of_nodes() == 0:
         typer.echo("Citation graph is empty — ingest some papers first.")
-        raise typer.Exit(0)
+        return
 
+    n_papers = len(known_ids)
     pr = citation_pagerank(graph)
     hubs = sorted(pr.items(), key=lambda x: -x[1])[:top_n]
-    dangling = dangling_citations(graph, known_ids)
+    dangling = sorted(dangling_citations(graph, known_ids))
 
     if output_json:
         typer.echo(
@@ -565,6 +569,12 @@ def graph_report(
         )
         return
 
+    if n_papers < settings.graph_min_corpus_size:
+        typer.echo(
+            f"Warning: only {n_papers} ingested papers "
+            f"(recommended ≥ {settings.graph_min_corpus_size} for meaningful ranking).",
+            err=True,
+        )
     typer.echo(f"Graph: {graph.number_of_nodes()} nodes, {graph.number_of_edges()} edges")
     typer.echo("")
     typer.echo(f"TOP-{top_n} HUB PAPERS (PageRank)")
diff --git a/codex/graph.py b/codex/graph.py
index 1d1e01d..78e694c 100644
--- a/codex/graph.py
+++ b/codex/graph.py
@@ -64,9 +64,13 @@ def citation_pagerank(graph: nx.DiGraph, *, damping: float = 0.85) -> dict[str,
     -------
     ``{paper_id: score}`` dict.  Scores sum to approximately 1.0.
 
-    When ``graph`` has fewer than :data:`_MIN_PAGERANK_NODES` nodes,
-    returns a uniform distribution and logs a warning — the signal is
-    too thin to be meaningful.
+    When ``graph`` has fewer than :data:`_MIN_PAGERANK_NODES` (5) nodes,
+    returns a uniform distribution and logs a warning — the graph is too
+    sparse for the random-walk model to converge meaningfully.  The
+    ``damping`` parameter is ignored in this branch.  The user-facing
+    corpus-size threshold (default 15) lives in
+    :attr:`codex.config.Settings.graph_min_corpus_size` and is surfaced
+    by ``codex graph report``.
     """
     n = graph.number_of_nodes()
     if n == 0:
diff --git a/tests/graph/test_cli.py b/tests/graph/test_cli.py
index b2460c0..32ee60c 100644
--- a/tests/graph/test_cli.py
+++ b/tests/graph/test_cli.py
@@ -47,7 +47,7 @@ def _sample_graph() -> nx.DiGraph:
 
 
 class TestGraphReport:
-    def _run(self, graph=None, known_ids=("A", "B", "C"), *extra_args):
+    def _run(self, graph=None, known_ids=("A", "B", "C"), extra_args=()):
         if graph is None:
             graph = _sample_graph()
         conn = _make_conn_with_paper_ids(*known_ids)
@@ -56,7 +56,7 @@ class TestGraphReport:
             patch("codex.db.get_conn", side_effect=_make_conn_cm(conn)),
             patch("codex.graph.build_citation_graph", return_value=graph),
         ):
-            return runner.invoke(app, ["graph", "report"])
+            return runner.invoke(app, ["graph", "report", *extra_args])
 
     def test_exit_zero(self):
         assert self._run().exit_code == 0
@@ -111,6 +111,12 @@ class TestGraphReport:
             result = runner.invoke(app, ["graph", "report", "--top-n", "3"])
         assert result.exit_code == 0
 
+    def test_small_corpus_warning_shown(self):
+        # known_ids has only 3 papers (< graph_min_corpus_size default 15)
+        result = self._run(known_ids=("A", "B", "C"))
+        assert result.exit_code == 0
+        assert "Warning" in result.output or "warning" in result.output
+
 
 # ---------------------------------------------------------------------------
 # codex graph related
@@ -118,7 +124,7 @@ class TestGraphReport:
 
 
 class TestGraphRelated:
-    def _run(self, paper_id: str, graph=None, *extra_args):
+    def _run(self, paper_id: str, graph=None, extra_args=()):
         if graph is None:
             graph = _sample_graph()
         conn = MagicMock()
@@ -137,12 +143,12 @@ class TestGraphRelated:
         assert "No papers" in result.output
 
     def test_no_results_message_when_min_shared_high(self):
-        result = self._run("A", _sample_graph(), "--min-shared", "99")
+        result = self._run("A", _sample_graph(), extra_args=("--min-shared", "99"))
         assert "No papers" in result.output
 
     def test_related_papers_shown(self):
         # A cites {B,C}; B also cites C → shared=1 with A
-        result = self._run("A", _sample_graph(), "--min-shared", "1")
+        result = self._run("A", _sample_graph(), extra_args=("--min-shared", "1"))
         assert result.exit_code == 0
         # B shares C with A; should appear
         assert "B" in result.output
@@ -154,15 +160,22 @@ class TestGraphRelated:
 
 
 class TestSearchCiteBoost:
+    def _make_two_paper_conn(self, pr_winner: str) -> MagicMock:
+        """Two papers A and B at equal distance; pr_winner has a high PageRank score."""
+        conn = MagicMock()
+        conn.execute.return_value.fetchall.return_value = [
+            {"id": "A", "title": "Paper A", "year": 2020, "distance": 0.5},
+            {"id": "B", "title": "Paper B", "year": 2021, "distance": 0.5},
+        ]
+        return conn
+
     def test_cite_boost_exits_zero(self):
         conn = MagicMock()
-        paper_rows = [
+        conn.execute.return_value.fetchall.return_value = [
             {"id": "A", "title": "Paper A", "year": 2020, "distance": 0.2},
         ]
         graph = _sample_graph()
 
-        conn.execute.return_value.fetchall.return_value = paper_rows
-
         def _fake_encode(texts):
             return np.zeros((len(texts), 1024), dtype="float32")
 
@@ -174,3 +187,38 @@ class TestSearchCiteBoost:
             mock_emb.return_value.encode_dense.side_effect = _fake_encode
             result = runner.invoke(app, ["search", "paper", "hodge star", "--cite-boost"])
         assert result.exit_code == 0
+
+    def test_cite_boost_promotes_high_pr_paper(self):
+        """High-PR paper should appear first (lower boosted distance) via divide formula."""
+        import networkx as nx
+
+        # Build a graph where A has high PageRank (many papers cite A)
+        g = nx.DiGraph()
+        for i in range(10):
+            g.add_edge(f"P{i}", "A")  # A is heavily cited → high PR
+        g.add_edge("P0", "B")  # B is barely cited → low PR
+
+        conn = MagicMock()
+        conn.execute.return_value.fetchall.return_value = [
+            {"id": "A", "title": "Hub", "year": 2020, "distance": 0.5},
+            {"id": "B", "title": "Niche", "year": 2021, "distance": 0.5},
+        ]
+
+        def _fake_encode(texts):
+            return np.zeros((len(texts), 1024), dtype="float32")
+
+        with (
+            patch("codex.db.get_conn", side_effect=_make_conn_cm(conn)),
+            patch("codex.embed.get_embedder") as mock_emb,
+            patch("codex.graph.build_citation_graph", return_value=g),
+        ):
+            mock_emb.return_value.encode_dense.side_effect = _fake_encode
+            result = runner.invoke(app, ["search", "paper", "query", "--cite-boost"])
+
+        assert result.exit_code == 0
+        lines = [ln for ln in result.output.strip().splitlines() if "Hub" in ln or "Niche" in ln]
+        assert len(lines) == 2
+        # A (Hub) should appear before B (Niche) — lower boosted distance
+        hub_line = next(line for line in lines if "Hub" in line)
+        niche_line = next(line for line in lines if "Niche" in line)
+        assert lines.index(hub_line) < lines.index(niche_line)