diff --git a/tests/search_eval/compare_runs.py b/tests/search_eval/compare_runs.py
new file mode 100644
index 0000000..db1b435
--- /dev/null
+++ b/tests/search_eval/compare_runs.py
@@ -0,0 +1,97 @@
+"""Phase 2A E-4 비교기 — baseline vs 후보 run CSV 들의 per-query 판정.
+
+    python tests/search_eval/compare_runs.py \
+        --baseline baselines/<exact 재측정>.csv \
+        --cand qwen06=<...>.csv --cand qwen4=<...>.csv --cand qwen4m=<...>.csv \
+        [--epsilon 0.01] [--bootstrap 2000]
+
+판정 출력(plan r3 E-4): 전체 graded NDCG 평균 delta · per-query win/loss/tie(|d|<ε=tie)
+· 부트스트랩 95% CI · 카테고리별 평균 · 상위 개선/퇴행 쿼리. failure_expected/error 행 제외.
+"""
+
+from __future__ import annotations
+
+import argparse
+import csv
+import random
+import statistics
+from pathlib import Path
+
+
+def load(path: str) -> dict[str, dict]:
+    out = {}
+    with Path(path).open(encoding="utf-8") as f:
+        for row in csv.DictReader(f):
+            if row.get("failure_expected", "").lower() in ("true", "1"):
+                continue
+            if row.get("error"):
+                continue
+            try:
+                row["_g"] = float(row["graded_ndcg_at_10"])
+            except (TypeError, ValueError):
+                continue
+            out[row["id"]] = row
+    return out
+
+
+def bootstrap_ci(deltas: list[float], n: int, seed: int = 42) -> tuple[float, float]:
+    rng = random.Random(seed)
+    means = sorted(
+        statistics.mean(rng.choices(deltas, k=len(deltas))) for _ in range(n)
+    )
+    return means[int(0.025 * n)], means[int(0.975 * n)]
+
+
+def main() -> None:
+    p = argparse.ArgumentParser()
+    p.add_argument("--baseline", required=True)
+    p.add_argument("--cand", action="append", required=True, metavar="name=csv")
+    p.add_argument("--epsilon", type=float, default=0.01)
+    p.add_argument("--bootstrap", type=int, default=2000)
+    a = p.parse_args()
+
+    base = load(a.baseline)
+    print(f"baseline: {a.baseline} — scored {len(base)}, "
+          f"graded NDCG mean {statistics.mean(r['_g'] for r in base.values()):.4f}")
+
+    for spec in a.cand:
+        name, path = spec.split("=", 1)
+        cand = load(path)
+        ids = sorted(set(base) & set(cand))
+        if len(ids) != len(base):
+            print(f"  ⚠ {name}: 교집합 {len(ids)} != baseline {len(base)} — 누락 쿼리 확인")
+        deltas = [cand[i]["_g"] - base[i]["_g"] for i in ids]
+        mean_b = statistics.mean(base[i]["_g"] for i in ids)
+        mean_c = statistics.mean(cand[i]["_g"] for i in ids)
+        win = sum(1 for d in deltas if d > a.epsilon)
+        loss = sum(1 for d in deltas if d < -a.epsilon)
+        tie = len(deltas) - win - loss
+        lo, hi = bootstrap_ci(deltas, a.bootstrap)
+        decided = win + loss
+        win_rate = (win / decided * 100) if decided else 0.0
+
+        print(f"\n== {name} ==")
+        print(f"  graded NDCG: {mean_b:.4f} → {mean_c:.4f}  (delta {mean_c-mean_b:+.4f}, "
+              f"bootstrap95% [{lo:+.4f}, {hi:+.4f}])")
+        print(f"  per-query: win {win} / loss {loss} / tie {tie} (ε={a.epsilon}) — "
+              f"win-rate(결정전) {win_rate:.0f}%")
+
+        cats: dict[str, list[float]] = {}
+        for i in ids:
+            cats.setdefault(base[i].get("category", "?"), []).append(
+                cand[i]["_g"] - base[i]["_g"]
+            )
+        for c in sorted(cats):
+            ds = cats[c]
+            cb = statistics.mean(base[i]["_g"] for i in ids if base[i].get("category") == c)
+            cc = statistics.mean(cand[i]["_g"] for i in ids if base[i].get("category") == c)
+            print(f"    {c:<18} {cb:.3f} → {cc:.3f} ({statistics.mean(ds):+.3f}, n={len(ds)})")
+
+        ranked = sorted(ids, key=lambda i: cand[i]["_g"] - base[i]["_g"])
+        worst = [(i, round(cand[i]['_g']-base[i]['_g'],3)) for i in ranked[:3]]
+        best = [(i, round(cand[i]['_g']-base[i]['_g'],3)) for i in ranked[-3:][::-1]]
+        print(f"    개선 top3 {best} / 퇴행 top3 {worst}")
+
+
+if __name__ == "__main__":
+    main()