diff --git a/backend/app/services/gallery_service.py b/backend/app/services/gallery_service.py
index ca2a7df..d4e923b 100644
--- a/backend/app/services/gallery_service.py
+++ b/backend/app/services/gallery_service.py
@@ -289,7 +289,7 @@ def _gallery_images(rows, artists: dict[int, dict]) -> list[GalleryImage]:
     ]
 
 
-def _diversify_similar(src, rows, limit, *, dup_threshold=6, lam=0.55):
+def _diversify_similar(src, rows, limit, *, dup_threshold=8, lam=0.40):
     """Trim a nearest-cosine candidate pool down to `limit` diverse picks.
 
     1. pHash collapse: drop any candidate whose perceptual hash is within
@@ -300,6 +300,11 @@ def _diversify_similar(src, rows, limit, *, dup_threshold=6, lam=0.55):
        the most relevant up top but pushes the selection to SPAN clusters
        instead of returning 40 variations of one image.
 
+    `lam` is the variance dial: lower = weight the diversity penalty harder, so
+    the rail reaches further across clusters (operator wanted MORE variance,
+    2026-07-01 — dropped 0.55→0.40, dup 6→8, paired with a wider pool in
+    `similar()`).
+
     Falls back to nearest-order (`rows[:limit]`) on any failure or a small pool.
     """
     if len(rows) <= 1:
@@ -658,8 +663,10 @@ class GalleryService:
             return []
 
         # Over-fetch so diversification has clusters to spread across — without a
-        # wide pool there's nothing but the near-dupes to choose from.
-        pool_n = min(200, max(limit * 5, 60))
+        # wide pool there's nothing but the near-dupes to choose from. Widened
+        # (5×→8×, cap 200→400) so the stronger MMR has genuinely distinct
+        # neighbourhoods to reach into for more variance (operator, 2026-07-01).
+        pool_n = min(400, max(limit * 8, 100))
         distance = ImageRecord.siglip_embedding.cosine_distance(src.siglip_embedding)
         eff = _effective_date_col()
         stmt = select(ImageRecord, Post.post_date, eff.label("eff"))