Pipelined runtime times out on a small nested COUNT query with CSV and relationship-vector search

a,b
one,two
three,four

DROP INDEX fuzz_node_vector_index IF EXISTS;
DROP INDEX fuzz_relationship_vector_index IF EXISTS;

CREATE VECTOR INDEX fuzz_node_vector_index
FOR (n:FuzzVector) ON n.embedding
OPTIONS {
  indexConfig: {
    `vector.dimensions`: 3,
    `vector.similarity_function`: 'cosine'
  }
};

CREATE VECTOR INDEX fuzz_relationship_vector_index
FOR ()-[r:FuzzVectorRel]-() ON r.embedding
OPTIONS {
  indexConfig: {
    `vector.dimensions`: 3,
    `vector.similarity_function`: 'cosine'
  }
};

CALL db.awaitIndexes();

CREATE (:FuzzVector {embedding: [1.0, 0.0, 0.0], rank: 1, active: true}),
       (:FuzzVector {embedding: [0.0, 1.0, 0.0], rank: 2, active: false});

MATCH (a:FuzzVector), (b:FuzzVector)
WHERE a.rank = 1 AND b.rank = 2
CREATE (a)-[:FuzzVectorRel {embedding: [1.0, 0.0, 0.0], rank: 1}]->(b);

UNWIND range(0, 63) AS i
CREATE (:l0:l1:l2:l3:l4:l5:l6:l7:l8:l9:l10:l11 {
  id: i,
  k0: true,
  k8: false,
  k9: i = 63
});

UNWIND range(0, 63) AS i
MATCH (a {id: i}),
      (b {id: CASE WHEN i = 63 THEN 0 ELSE i + 1 END})
CREATE (a)-[:rt9 {k8: false}]->(b);

CYPHER runtime=pipelined
CREATE ()-[:rt9 {
  k1: COUNT {
    MATCH (), ({k0: true})-[r3 {k8: false}]-()
    WHERE EXISTS {
      CALL () {
        LOAD CSV FROM 'file:///fuzz.csv' AS a
        RETURN 1 AS x
      }
      SKIP 0
    }
    AND COUNT {
      MATCH ()-[r5]->()
      SEARCH r5 IN (VECTOR INDEX fuzz_relationship_vector_index FOR [1.0, 0.0, 0.0] LIMIT 3)
    } >= 0
  }
}]->()
RETURN 1 AS x;