diff --git a/docs/performance.md b/docs/performance.md
index 87b7162..d77d628 100644
--- a/docs/performance.md
+++ b/docs/performance.md
@@ -6,7 +6,7 @@ Here's a contrived rough benchmark to show the magnitude differences between Pyt
 
 **Spoiler**: GPU-backed KlongPy is about 790x faster than naive Python and 36x faster than NumPy-backed KlongPy.
 
-### Python
+## Python
 
 ```python
 def python_vec(number=100):
@@ -14,7 +14,7 @@ def python_vec(number=100):
     return r/number
 ```
 
-### KlongPy
+## KlongPy
 
 ```python
 # NumPy and CuPy (CuPy is enabled via USE_GPU=1 environment variable
@@ -24,7 +24,7 @@ def klong_vec(number=100):
     return r/number
 ```
 
-### NumPy (explicit usage)
+## NumPy (explicit usage)
 
 ```python
 def NumPy_vec(number=100):
@@ -36,19 +36,22 @@ def NumPy_vec(number=100):
 
 ### CPU (AMD Ryzen 9 7950x)
 
-    $ python3 tests/perf_vector.py
-    Python: 0.369111s
-    KlongPy USE_GPU=None: 0.017946s
-    Numpy: 0.017896s
-    Python / KlongPy => 20.568334
-    Numpy / KlongPy => 0.997245
+```bash
+$ python3 tests/perf_vector.py
+Python: 0.369111s
+KlongPy USE_GPU=None: 0.017946s
+Numpy: 0.017896s
+Python / KlongPy => 20.568334
+Numpy / KlongPy => 0.997245
+```
 
 ### GPU (Same CPU with NVIDIA GeForce RTX 3090)
 
-    $ USE_GPU=1 python3 tests/perf_vector.py
-    Python: 0.364893s
-    KlongPy USE_GPU=1: 0.000461s
-    NumPy: 0.017053s
-    Python / KlongPy => 790.678069
-    Numpy / KlongPy => 36.951443
-
+```bash
+$ USE_GPU=1 python3 tests/perf_vector.py
+Python: 0.364893s
+KlongPy USE_GPU=1: 0.000461s
+NumPy: 0.017053s
+Python / KlongPy => 790.678069
+Numpy / KlongPy => 36.951443
+```
diff --git a/klongpy/dyads.py b/klongpy/dyads.py
index 85ab815..1ab7417 100644
--- a/klongpy/dyads.py
+++ b/klongpy/dyads.py
@@ -478,14 +478,8 @@ def eval_dyad_integer_divide(a, b):
 
 
 def _arr_to_list(a):
-    if np.isarray(a):
-        if a.ndim == 1:
-            return a
-        elif a.shape[0] == 1:
-            return [a.flatten()] # TODO: test squeeze
-        elif a.shape[0] > 1:
-            return a
-    return [a] if not is_list(a) else a
+    """ Convert a to a list if it is not already one """
+    return a if is_list(a) else [a]# if not is_list(a) else a
 
 
 def eval_dyad_join(a, b):
diff --git a/tests/kgtests/language/test_nested_join.kg b/tests/kgtests/language/test_nested_join.kg
new file mode 100644
index 0000000..48709e0
--- /dev/null
+++ b/tests/kgtests/language/test_nested_join.kg
@@ -0,0 +1,10 @@
+:" test that we can create new arrays with nested arrays and the result has the right shape "
+
+data::{!3}
+t("1,,{data()}'!2";1,,{data()}'!2;[1 [[0 1 2] [0 1 2]]])
+
+
+a::!10
+k::3
+c::(,,,1#a),k
+t("c::(,,,1#a),k";c::(,,,1#a),k;[[[[0]]] 3])
diff --git a/tests/test_known_bugs.py b/tests/test_known_bugs.py
index 3500c1f..d1a8860 100644
--- a/tests/test_known_bugs.py
+++ b/tests/test_known_bugs.py
@@ -39,13 +39,6 @@ def test_table_access_with_at(self):
         r = klong('T@"col1"')
         self.assertTrue(kg_equal(r, data['col1']))
 
-    @unittest.skip
-    def test_join_nested_array(self):
-        klong = KlongInterpreter()
-        r = klong("a::!10;k::3;c::(,,,1#a),k")
-        # currently this flattens to [[0], 3]
-        self.assertTrue(kg_equal(r,[[[[0]]],3]))
-
     @unittest.skip
     def test_extra_spaces(self):
         klong = KlongInterpreter()