Filter deleted nodes from DiskANN search results and add delete tests

DiskANN's delete repair only fixes forward edges (nodes the deleted
node pointed to). Stale reverse edges can cause deleted rowids to
appear in search results. Fix: track a 'confirmed' flag on each
search candidate, set when the full-precision vector is successfully
read during re-ranking. Only confirmed candidates are included in
output. Zero additional SQL queries — piggybacks on the existing
re-rank vector read.

Also adds delete hardening tests:
- Rescore: interleaved delete+KNN, rowid_in after deletes, full
  delete+reinsert cycle
- DiskANN: delete+reinsert cycles with KNN verification, interleaved
  delete+KNN

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

tests/test-diskann.py

@@ -1176,3 +1176,73 @@ def test_corrupt_truncated_node_blob(db):
         ).fetchall()
     except sqlite3.OperationalError:
         pass  # Error is acceptable — crash is not
+
+
+def test_diskann_delete_reinsert_cycle_knn(db):
+    """Repeatedly delete and reinsert rows, verify KNN stays correct."""
+    import random
+    random.seed(101)
+    db.execute("""
+        CREATE VIRTUAL TABLE t USING vec0(
+            emb float[8] INDEXED BY diskann(neighbor_quantizer=binary, n_neighbors=8)
+        )
+    """)
+    N = 30
+    vecs = {}
+    for i in range(1, N + 1):
+        v = [random.gauss(0, 1) for _ in range(8)]
+        vecs[i] = v
+        db.execute("INSERT INTO t(rowid, emb) VALUES (?, ?)", [i, _f32(v)])
+
+    # 3 cycles: delete half, reinsert with new vectors, verify KNN
+    for cycle in range(3):
+        to_delete = random.sample(sorted(vecs.keys()), len(vecs) // 2)
+        for r in to_delete:
+            db.execute("DELETE FROM t WHERE rowid = ?", [r])
+            del vecs[r]
+
+        # Reinsert with new rowids
+        new_start = 100 + cycle * 50
+        for i in range(len(to_delete)):
+            rid = new_start + i
+            v = [random.gauss(0, 1) for _ in range(8)]
+            vecs[rid] = v
+            db.execute("INSERT INTO t(rowid, emb) VALUES (?, ?)", [rid, _f32(v)])
+
+        # KNN should return only alive rows
+        query = [0.0] * 8
+        rows = db.execute(
+            "SELECT rowid FROM t WHERE emb MATCH ? AND k=10",
+            [_f32(query)],
+        ).fetchall()
+        returned = {r["rowid"] for r in rows}
+        assert returned.issubset(set(vecs.keys())), \
+            f"Cycle {cycle}: deleted rowid in KNN results"
+        assert len(rows) >= 1
+
+
+def test_diskann_delete_interleaved_with_knn(db):
+    """Delete one row at a time, querying KNN after each delete."""
+    db.execute("""
+        CREATE VIRTUAL TABLE t USING vec0(
+            emb float[8] INDEXED BY diskann(neighbor_quantizer=binary, n_neighbors=8)
+        )
+    """)
+    N = 20
+    for i in range(1, N + 1):
+        vec = [0.0] * 8
+        vec[i % 8] = float(i)
+        db.execute("INSERT INTO t(rowid, emb) VALUES (?, ?)", [i, _f32(vec)])
+
+    alive = set(range(1, N + 1))
+    for to_del in [1, 5, 10, 15, 20]:
+        db.execute("DELETE FROM t WHERE rowid = ?", [to_del])
+        alive.discard(to_del)
+
+        rows = db.execute(
+            "SELECT rowid FROM t WHERE emb MATCH ? AND k=5",
+            [_f32([1, 0, 0, 0, 0, 0, 0, 0])],
+        ).fetchall()
+        returned = {r["rowid"] for r in rows}
+        assert returned.issubset(alive), \
+            f"Deleted rowid {to_del} found in KNN results"