backend/app/services/ml/ccip.py

@@ -161,16 +161,22 @@ async def match_image(
     if threshold is None:
         threshold = await _settings_threshold(session)
 
-    qvecs = (
+    # Keep each figure region's bbox alongside its vector so a match can point at
+    # the figure that matched (#1206 grounding), not just the score.
+    fig_rows = (
         await session.execute(
-            select(ImageRegion.ccip_embedding).where(
+            select(
+                ImageRegion.ccip_embedding,
+                ImageRegion.rx, ImageRegion.ry, ImageRegion.rw, ImageRegion.rh,
+                ImageRegion.kind, ImageRegion.detector_version,
+            ).where(
                 ImageRegion.image_record_id == image_id,
                 ImageRegion.kind.in_(_FIGURE_KINDS),
                 ImageRegion.ccip_embedding.is_not(None),
             )
         )
-    ).scalars().all()
-    if not qvecs:
+    ).all()
+    if not fig_rows:
         return []
     refs = await character_references(session)
     if not refs:
@@ -186,13 +192,21 @@ async def match_image(
     )
     names = await _tag_names(session, [t for t in refs if t not in applied])
 
+    qvecs = [r[0] for r in fig_rows]
+    fig_meta = [
+        {"bbox": [rx, ry, rw, rh], "kind": kind, "detector": detector}
+        for _v, rx, ry, rw, rh, kind, detector in fig_rows
+    ]
     Q = _l2norm(np.vstack([np.asarray(v, dtype=np.float32) for v in qvecs]), np)
     out = []
     for tag_id, vecs in refs.items():
         if tag_id in applied:
             continue
         R = _l2norm(np.vstack([np.asarray(v, dtype=np.float32) for v in vecs]), np)
-        best = float((Q @ R.T).max())   # best (query figure, reference) cosine
+        sims = Q @ R.T                       # (n_query_figures, n_references)
+        per_figure = sims.max(axis=1)        # best reference cosine per figure
+        best_figure = int(per_figure.argmax())
+        best = float(per_figure[best_figure])
         if best >= threshold:
             out.append({
                 "tag_id": tag_id,
@@ -200,6 +214,8 @@ async def match_image(
                 "category": "character",
                 "score": round(best, 4),
                 "source": "ccip",
+                # the figure region that matched → grounds the character tag.
+                "grounding": fig_meta[best_figure],
             })
     out.sort(key=lambda d: d["score"], reverse=True)
     return out

backend/app/services/ml/suggestions.py

@@ -116,9 +116,13 @@ class SuggestionService:
             if ex is not None:
                 ex["source"] = "both"
                 ex["score"] = max(ex["score"], c["score"])
+                # Keep the head's localized crop if it had one; else fall back to
+                # the CCIP figure so a corroborated character still grounds (#1206).
+                ex["grounding"] = ex.get("grounding") or c.get("grounding")
             else:
                 merged[key] = {
                     "name": c["name"], "score": c["score"], "source": "ccip",
+                    "grounding": c.get("grounding"),
                 }
 
         result = SuggestionList()

tests/test_ccip.py

@@ -55,6 +55,10 @@ async def test_matches_same_character_across_images(db):
     m = next(x for x in matches if x["tag_id"] == raven.id)
     assert m["source"] == "ccip" and m["category"] == "character"
     assert m["score"] > 0.9
+    # #1206: the match grounds to the figure region that matched (hover → the
+    # figure box lights up), so a character suggestion is localized too.
+    assert m["grounding"]["bbox"] == pytest.approx([0.0, 0.0, 1.0, 1.0])
+    assert m["grounding"]["kind"] == "figure"
 
 
 @pytest.mark.asyncio