diff --git a/cauldron/consolidate_foods.py b/cauldron/consolidate_foods.py
index c7262ff..c0cc136 100644
--- a/cauldron/consolidate_foods.py
+++ b/cauldron/consolidate_foods.py
@@ -88,38 +88,32 @@ def _foods_in_household(mealie: Mealie, household_id: str) -> list[dict]:
 
 
 def _cluster(foods: list[dict], threshold: int = CLUSTER_THRESHOLD) -> list[list[dict]]:
-    """Single-link agglomerative clustering on token_set_ratio. O(n²) — fine
-    for ~3000 foods (~9M comparisons). Returns clusters of size ≥ 2."""
+    """Pair-based: emit one 2-food candidate per (i, j) where token_set_ratio
+    >= threshold. Replaces the original single-link agglomerative which
+    produced a 50+ food megacluster on Cobb's catalog by chaining weak
+    similarities (`2% milk` → `acai berry` → `acai berry juice` → ...).
+
+    Each emitted pair is a clean Sonnet-decision unit — easier prompt,
+    higher accuracy, uniform UI cards. The trade-off (3-way dupes get
+    split into 3 pairs that go through review separately) is fine —
+    Mealie's merge endpoint is per-pair anyway, and the apply path
+    defensively skips a pair whose canonical_id was already merged
+    away by an earlier pair.
+
+    For ~3000 foods this is ~4M comparisons in pure Python — runs in
+    a few seconds."""
     n = len(foods)
-    parent = list(range(n))
-
-    def find(x):
-        while parent[x] != x:
-            parent[x] = parent[parent[x]]
-            x = parent[x]
-        return x
-
-    def union(a, b):
-        ra, rb = find(a), find(b)
-        if ra != rb:
-            parent[ra] = rb
-
     names = [(f.get("name") or "").strip().lower() for f in foods]
+    pairs: list[list[dict]] = []
     for i in range(n):
         if not names[i]:
             continue
         for j in range(i + 1, n):
             if not names[j]:
                 continue
-            score = fuzz.token_set_ratio(names[i], names[j])
-            if score >= threshold:
-                union(i, j)
-
-    groups: dict[int, list[dict]] = {}
-    for i in range(n):
-        r = find(i)
-        groups.setdefault(r, []).append(foods[i])
-    return [g for g in groups.values() if len(g) >= 2]
+            if fuzz.token_set_ratio(names[i], names[j]) >= threshold:
+                pairs.append([foods[i], foods[j]])
+    return pairs
 
 
 def _cluster_key(cluster: list[dict]) -> str:
@@ -231,6 +225,14 @@ def run_apply(*, db: DB, job_id: int, mealie: Mealie) -> None:
                 try:
                     mealie.merge_foods(from_id=did, to_id=canonical_id)
                 except MealieError as e:
+                    msg = str(e)
+                    # Pair-based clustering can emit overlapping pairs:
+                    # if (A,B) was already approved+merged, a later (A,C)
+                    # pair has stale A. Mealie returns 404 — treat that
+                    # as already-handled, not an error.
+                    if "404" in msg or "not found" in msg.lower():
+                        log.info("[consolidate:%s] merge %s → %s: stale (already merged elsewhere)", job_id, did, canonical_id)
+                        continue
                     err = f"merge {did} → {canonical_id}: {e}"
                     log.warning("[consolidate:%s] %s", job_id, err)
                     break