diff --git a/reference_model/src/generate/generate_pseudo_random.cc b/reference_model/src/generate/generate_pseudo_random.cc
index f234796..78013eb 100644
--- a/reference_model/src/generate/generate_pseudo_random.cc
+++ b/reference_model/src/generate/generate_pseudo_random.cc
@@ -126,9 +126,10 @@
         WARNING("[Generator][PR] Unknown operator.");
         return false;
     }
-    if (cfg.pseudoRandomInfo.range.size() != 0 || cfg.pseudoRandomInfo.range.size() != 2)
+    if (cfg.pseudoRandomInfo.range.size() != 0 && cfg.pseudoRandomInfo.range.size() != 2)
     {
-        WARNING("[Generator][PR] Invalid range.");
+        WARNING("[Generator][PR] Invalid range");
+        return false;
     }
 
     switch (cfg.dataType)
diff --git a/reference_model/src/generate/generate_utils.cc b/reference_model/src/generate/generate_utils.cc
index ae6dfcb..8ae889a 100644
--- a/reference_model/src/generate/generate_utils.cc
+++ b/reference_model/src/generate/generate_utils.cc
@@ -38,11 +38,15 @@
 NLOHMANN_JSON_SERIALIZE_ENUM(Op,
                              {
                                  { Op::Op_UNKNOWN, "UNKNOWN" },
+                                 { Op::Op_ADD, "ADD" },
                                  { Op::Op_CONV2D, "CONV2D" },
                                  { Op::Op_MATMUL, "MATMUL" },
+                                 { Op::Op_MAXIMUM, "MAXIMUM" },
                                  { Op::Op_MAX_POOL2D, "MAX_POOL2D" },
+                                 { Op::Op_MINIMUM, "MINIMUM" },
                                  { Op::Op_MUL, "MUL" },
                                  { Op::Op_PAD, "PAD" },
+                                 { Op::Op_SUB, "SUB" },
                              })
 
 }    // namespace tosa
diff --git a/verif/conformance/tosa_main_profile_ops_info.json b/verif/conformance/tosa_main_profile_ops_info.json
index 4256bfb..254f5e7 100644
--- a/verif/conformance/tosa_main_profile_ops_info.json
+++ b/verif/conformance/tosa_main_profile_ops_info.json
@@ -69,6 +69,7 @@
         "profile": [
             "tosa-mi"
         ],
+        "support_for": [ "lazy_data_gen" ],
         "generation": {
             "standard": {
                 "generator_args": [
@@ -80,7 +81,7 @@
                         "--target-dtype",
                         "bf16",
                         "--fp-values-range",
-                        "-2.0,2.0",
+                        "-max,max",
                         "--tensor-dim-range",
                         "16,64",
                         "--target-rank",
@@ -94,7 +95,7 @@
                         "--target-dtype",
                         "fp32",
                         "--fp-values-range",
-                        "-2.0,2.0",
+                        "-max,max",
                         "--tensor-dim-range",
                         "1,16",
                         "--target-rank",
@@ -106,7 +107,7 @@
                         "--target-dtype",
                         "fp16",
                         "--fp-values-range",
-                        "-2.0,2.0",
+                        "-max,max",
                         "--target-shape",
                         "2,1,1,65532",
                         "--target-shape",
@@ -2182,6 +2183,7 @@
         "profile": [
             "tosa-mi"
         ],
+        "support_for": [ "lazy_data_gen" ],
         "generation": {
             "standard": {
                 "generator_args": [
@@ -2193,7 +2195,7 @@
                         "--target-dtype",
                         "bf16",
                         "--fp-values-range",
-                        "-2.0,2.0",
+                        "-max,max",
                         "--tensor-dim-range",
                         "16,64",
                         "--target-rank",
@@ -2211,7 +2213,7 @@
                         "--target-dtype",
                         "bf16",
                         "--fp-values-range",
-                        "-2.0,2.0",
+                        "-max,max",
                         "--tensor-dim-range",
                         "1,16",
                         "--target-rank",
@@ -2223,7 +2225,7 @@
                         "--target-dtype",
                         "fp32",
                         "--fp-values-range",
-                        "-2.0,2.0",
+                        "-max,max",
                         "--target-shape",
                         "65531,1,2,1",
                         "--target-shape",
@@ -2247,6 +2249,7 @@
         "profile": [
             "tosa-mi"
         ],
+        "support_for": [ "lazy_data_gen" ],
         "generation": {
             "standard": {
                 "generator_args": [
@@ -2258,7 +2261,7 @@
                         "--target-dtype",
                         "bf16",
                         "--fp-values-range",
-                        "-2.0,2.0",
+                        "-max,max",
                         "--tensor-dim-range",
                         "16,64",
                         "--target-rank",
@@ -2276,7 +2279,7 @@
                         "--target-dtype",
                         "bf16",
                         "--fp-values-range",
-                        "-2.0,2.0",
+                        "-max,max",
                         "--tensor-dim-range",
                         "1,16",
                         "--target-rank",
@@ -2288,7 +2291,7 @@
                         "--target-dtype",
                         "fp32",
                         "--fp-values-range",
-                        "-2.0,2.0",
+                        "-max,max",
                         "--target-shape",
                         "1,65531,2,1",
                         "--target-shape",
@@ -2775,6 +2778,7 @@
         "profile": [
             "tosa-mi"
         ],
+        "support_for": [ "lazy_data_gen" ],
         "generation": {
             "standard": {
                 "generator_args": [
@@ -2786,7 +2790,7 @@
                         "--target-dtype",
                         "bf16",
                         "--fp-values-range",
-                        "-2.0,2.0",
+                        "-max,max",
                         "--tensor-dim-range",
                         "16,64",
                         "--target-rank",
@@ -2800,7 +2804,7 @@
                         "--target-dtype",
                         "bf16",
                         "--fp-values-range",
-                        "-2.0,2.0",
+                        "-max,max",
                         "--tensor-dim-range",
                         "1,16",
                         "--target-rank",
@@ -2812,7 +2816,7 @@
                         "--target-dtype",
                         "fp32",
                         "--fp-values-range",
-                        "-2.0,2.0",
+                        "-max,max",
                         "--target-shape",
                         "1,1,65533,3",
                         "--target-shape",
diff --git a/verif/generator/tosa_arg_gen.py b/verif/generator/tosa_arg_gen.py
index 94b7172..1be243c 100644
--- a/verif/generator/tosa_arg_gen.py
+++ b/verif/generator/tosa_arg_gen.py
@@ -636,6 +636,19 @@
     }
 
     @staticmethod
+    def _get_data_range(testGen, dtype, highValueLookup):
+        if dtype in highValueLookup:
+            data_range = testGen.getDTypeRange(dtype, high_inclusive=True)
+            high_val = highValueLookup[dtype]
+            # Set the values to something that won't produce infinity whilst
+            # respecting the default ranges if less than the high value
+            return [
+                max(-high_val, data_range[0]),
+                min(high_val, data_range[1]),
+            ]
+        return None
+
+    @staticmethod
     def tvgLazyGenDefault(
         testGen, opName, dtypeList, shapeList, argsDict, error_name=None
     ):
@@ -777,16 +790,24 @@
                 testGen, op, dtypeList, shapeList, testArgs, error_name
             )
 
+    # Set the data range to half the largest value
+    TVG_FLOAT_HIGH_VALUE_ADDSUB = {
+        DType.FP32: (TVG_FLOAT_HIGH_VALUE[DType.FP32] / 2),
+        DType.FP16: (TVG_FLOAT_HIGH_VALUE[DType.FP16] / 2),
+        DType.BF16: (TVG_FLOAT_HIGH_VALUE[DType.BF16] / 2),
+    }
+
     @staticmethod
-    def tvgAddSub(testGen, op, dtypeList, shapeList, testArgs, error_name=None):
+    def tvgAddSub(testGen, opName, dtypeList, shapeList, argsDict, error_name=None):
         if dtypeList[0] == DType.INT32 and error_name is None:
-            # Make sure the operation does not cause value saturation - where
+            # Make sure the integer operation does not cause value saturation - where
             # the number wraps due to limited number of bits to store the answer
+            op = testGen.TOSA_OP_LIST[opName]
             pCount, cCount = op["operands"]
             assert (
                 pCount == 2 and cCount == 0
             ), "Op.ADD / Op.SUB must have 2 placeholders, 0 consts"
-            placeholders = []
+            tens_ser_list = []
             add = op["op"] == Op.ADD
             a_arr = testGen.getRandTensor(shapeList[0], dtypeList[0])
             b_arr = testGen.getRandTensor(shapeList[1], dtypeList[1])
@@ -833,17 +854,24 @@
                         ), "Op.ADD / SUB dimension must be 1 or matching to be broadcastable"
                         b_unsat_arr = np.amax(b_unsat_arr, axis=axis, keepdims=True)
 
-            placeholders.append(
+            tens_ser_list.append(
                 testGen.ser.addPlaceholder(shapeList[0], dtypeList[0], a_arr)
             )
-            placeholders.append(
+            tens_ser_list.append(
                 testGen.ser.addPlaceholder(shapeList[1], dtypeList[1], b_unsat_arr)
             )
 
-            return placeholders
+            return TosaTensorValuesGen.TVGInfo(tens_ser_list, None)
         else:
-            return TosaTensorValuesGen.tvgDefault(
-                testGen, op, dtypeList, shapeList, testArgs, error_name
+            # ERROR_IF or floating point test
+            data_range = TosaTensorValuesGen._get_data_range(
+                testGen, dtypeList[0], TosaTensorValuesGen.TVG_FLOAT_HIGH_VALUE_ADDSUB
+            )
+            if data_range:
+                argsDict["data_range"] = data_range
+
+            return TosaTensorValuesGen.tvgLazyGenDefault(
+                testGen, opName, dtypeList, shapeList, argsDict, error_name
             )
 
     @staticmethod
@@ -923,14 +951,15 @@
         )
 
     @staticmethod
-    def tvgIntDiv(testGen, op, dtypeList, shapeList, testArgs, error_name=None):
+    def tvgIntDiv(testGen, opName, dtypeList, shapeList, argsDict, error_name=None):
         if error_name is None:
+            op = testGen.TOSA_OP_LIST[opName]
             pCount, cCount = op["operands"]
             assert (
                 pCount == 2 and cCount == 0
             ), "Op.INTDIV must have 2 placeholders, 0 consts"
 
-            placeholders = []
+            tens_ser_list = []
 
             # Two invalid cases for Op.INTDIV:
             # 1. divisor == 0
@@ -947,17 +976,17 @@
 
                 break
 
-            placeholders.append(
+            tens_ser_list.append(
                 testGen.ser.addPlaceholder(shapeList[0], dtypeList[0], dividend_arr)
             )
-            placeholders.append(
+            tens_ser_list.append(
                 testGen.ser.addPlaceholder(shapeList[1], dtypeList[1], divisor_arr)
             )
 
-            return placeholders
+            return TosaTensorValuesGen.TVGInfo(tens_ser_list, None)
         else:
-            return TosaTensorValuesGen.tvgDefault(
-                testGen, op, dtypeList, shapeList, testArgs, error_name
+            return TosaTensorValuesGen.tvgLazyGenDefault(
+                testGen, opName, dtypeList, shapeList, argsDict, error_name
             )
 
     # Set the data range to the square root of the largest value
@@ -975,15 +1004,12 @@
             DType.FP32,
         ):
             # ERROR_IF or floating point test
-            if dtypeList[0] in TosaTensorValuesGen.TVG_FLOAT_HIGH_VALUE_MUL:
-                data_range = testGen.getDTypeRange(dtypeList[0], high_inclusive=True)
-                high_val = TosaTensorValuesGen.TVG_FLOAT_HIGH_VALUE_MUL[dtypeList[0]]
-                # Set the values to something that won't produce infinity whilst
-                # respecting the default ranges if less than the high value
-                argsDict["data_range"] = [
-                    max(-high_val, data_range[0]),
-                    min(high_val, data_range[1]),
-                ]
+            data_range = TosaTensorValuesGen._get_data_range(
+                testGen, dtypeList[0], TosaTensorValuesGen.TVG_FLOAT_HIGH_VALUE_MUL
+            )
+            if data_range:
+                argsDict["data_range"] = data_range
+
             return TosaTensorValuesGen.tvgLazyGenDefault(
                 testGen, opName, dtypeList, shapeList, argsDict, error_name
             )
@@ -1075,22 +1101,25 @@
         return tens
 
     @staticmethod
-    def tvgLogicalShift(testGen, op, dtypeList, shapeList, testArgs, error_name=None):
+    def tvgLogicalShift(
+        testGen, opName, dtypeList, shapeList, argsDict, error_name=None
+    ):
+        op = testGen.TOSA_OP_LIST[opName]
         pCount, cCount = op["operands"]
         assert (
             pCount == 2 and cCount == 0
         ), "Op.LOGICAL_LEFT_SHIFT or Op.LOGICAL_RIGHT_SHIFT must have 2 placeholders, 0 consts"
         values_arr = testGen.getRandTensor(shapeList[0], dtypeList[0])
         shift_arr = np.int32(testGen.rng.integers(low=0, high=32, size=shapeList[1]))
-        placeholders = []
-        placeholders.append(
+        tens_ser_list = []
+        tens_ser_list.append(
             testGen.ser.addPlaceholder(shapeList[0], dtypeList[0], values_arr)
         )
-        placeholders.append(
+        tens_ser_list.append(
             testGen.ser.addPlaceholder(shapeList[1], dtypeList[1], shift_arr)
         )
 
-        return placeholders
+        return TosaTensorValuesGen.TVGInfo(tens_ser_list, None)
 
     @staticmethod
     def tvgEqual(testGen, op, dtypeList, shapeList, testArgs, error_name=None):
@@ -1218,7 +1247,15 @@
     def agNone(testGen, opName, shapeList, dtype, error_name=None):
         """A trivial argument generator for operators that don't take any
         non-tensor arguments"""
-        return [("", [])]
+        arg_list = TosaArgGen._add_data_generators(
+            testGen,
+            opName,
+            dtype,
+            [("", {})],
+            error_name,
+        )
+        # Return list of tuples: (arg_str, args_dict)
+        return arg_list
 
     @staticmethod
     def agAxis(testGen, opName, shapeList, dtype, error_name=None):
diff --git a/verif/generator/tosa_test_gen.py b/verif/generator/tosa_test_gen.py
index 1995cbc..556a0d8 100644
--- a/verif/generator/tosa_test_gen.py
+++ b/verif/generator/tosa_test_gen.py
@@ -403,14 +403,18 @@
         self.ser.addOperator(op["op"], input_list, output_list, attr)
         return result_tens
 
-    def build_binary_broadcast(self, op, a, b, validator_fcns, error_name=None):
-        result_tens = OutputShaper.binaryBroadcastOp(
+    def build_binary_broadcast(
+        self, op, inputs, args_dict, validator_fcns, error_name=None, qinfo=None
+    ):
+        assert len(inputs) == 2
+        a, b = inputs
+        result_tensor = OutputShaper.binaryBroadcastOp(
             self.ser, self.rng, a, b, error_name
         )
 
         # Invalidate Input/Output list for error if checks.
         input_list = [a.name, b.name]
-        output_list = [result_tens.name]
+        output_list = [result_tensor.name]
         pCount, cCount = op["operands"]
         num_operands = pCount + cCount
         input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(
@@ -425,8 +429,8 @@
             input1=a,
             input2=b,
             input_dtype=a.dtype,
-            output_dtype=result_tens.dtype,
-            result_tensors=[result_tens],
+            output_dtype=result_tensor.dtype,
+            result_tensors=[result_tensor],
             input_list=input_list,
             output_list=output_list,
             num_operands=num_operands,
@@ -434,7 +438,16 @@
             return None
 
         self.ser.addOperator(op["op"], input_list, output_list)
-        return result_tens
+
+        if op["op"] == Op.POW:
+            # TODO - add compliance support
+            compliance = None
+        else:
+            compliance = self.tensorComplianceMetaData(
+                op, a.dtype, args_dict, result_tensor, error_name
+            )
+
+        return TosaTestGen.BuildInfo(result_tensor, compliance)
 
     def build_binary_nonbroadcast(self, op, a, b, validator_fcns=None, error_name=None):
         result_tens = OutputShaper.binaryNonBroadcastOp(self.ser, a, b)
@@ -3208,7 +3221,7 @@
                 build_binary_broadcast,
                 TosaTensorGen.tgBroadcastFuzz,
                 TosaTensorValuesGen.tvgAddSub,
-                None,
+                TosaArgGen.agNone,
             ),
             "types": TYPE_FI32,
             "error_if_validators": (
@@ -3220,6 +3233,10 @@
                 TosaErrorValidator.evDimensionMismatch,
                 TosaErrorValidator.evBroadcastShapesMismatch,
             ),
+            "data_gen": {
+                "fp": (gtu.DataGenType.PSEUDO_RANDOM,),
+            },
+            "compliance": {"ulp": 0.5},
         },
         "arithmetic_right_shift": {
             "op": Op.ARITHMETIC_RIGHT_SHIFT,
@@ -3247,8 +3264,8 @@
             "build_fcn": (
                 build_binary_broadcast,
                 TosaTensorGen.tgBroadcastFuzz,
-                TosaTensorValuesGen.tvgDefault,
-                None,
+                TosaTensorValuesGen.tvgLazyGenDefault,
+                TosaArgGen.agNone,
             ),
             "types": TYPE_INT,
             "error_if_validators": (
@@ -3267,8 +3284,8 @@
             "build_fcn": (
                 build_binary_broadcast,
                 TosaTensorGen.tgBroadcastFuzz,
-                TosaTensorValuesGen.tvgDefault,
-                None,
+                TosaTensorValuesGen.tvgLazyGenDefault,
+                TosaArgGen.agNone,
             ),
             "types": TYPE_INT,
             "error_if_validators": (
@@ -3287,8 +3304,8 @@
             "build_fcn": (
                 build_binary_broadcast,
                 TosaTensorGen.tgBroadcastFuzz,
-                TosaTensorValuesGen.tvgDefault,
-                None,
+                TosaTensorValuesGen.tvgLazyGenDefault,
+                TosaArgGen.agNone,
             ),
             "types": TYPE_INT,
             "error_if_validators": (
@@ -3308,7 +3325,7 @@
                 build_binary_broadcast,
                 TosaTensorGen.tgBroadcastFuzz,
                 TosaTensorValuesGen.tvgIntDiv,
-                None,
+                TosaArgGen.agNone,
             ),
             "types": [DType.INT32],
             "error_if_validators": (
@@ -3327,8 +3344,8 @@
             "build_fcn": (
                 build_binary_broadcast,
                 TosaTensorGen.tgBroadcastFuzz,
-                TosaTensorValuesGen.tvgDefault,
-                None,
+                TosaTensorValuesGen.tvgLazyGenDefault,
+                TosaArgGen.agNone,
             ),
             "types": TYPE_BOOL,
             "error_if_validators": (
@@ -3348,7 +3365,7 @@
                 build_binary_broadcast,
                 TosaTensorGen.tgBroadcastFuzz,
                 TosaTensorValuesGen.tvgLogicalShift,
-                None,
+                TosaArgGen.agNone,
             ),
             "types": TYPE_INT,
             "error_if_validators": (
@@ -3368,7 +3385,7 @@
                 build_binary_broadcast,
                 TosaTensorGen.tgBroadcastFuzz,
                 TosaTensorValuesGen.tvgLogicalShift,
-                None,
+                TosaArgGen.agNone,
             ),
             "types": TYPE_INT,
             "error_if_validators": (
@@ -3387,8 +3404,8 @@
             "build_fcn": (
                 build_binary_broadcast,
                 TosaTensorGen.tgBroadcastFuzz,
-                TosaTensorValuesGen.tvgDefault,
-                None,
+                TosaTensorValuesGen.tvgLazyGenDefault,
+                TosaArgGen.agNone,
             ),
             "types": TYPE_BOOL,
             "error_if_validators": (
@@ -3407,8 +3424,8 @@
             "build_fcn": (
                 build_binary_broadcast,
                 TosaTensorGen.tgBroadcastFuzz,
-                TosaTensorValuesGen.tvgDefault,
-                None,
+                TosaTensorValuesGen.tvgLazyGenDefault,
+                TosaArgGen.agNone,
             ),
             "types": TYPE_BOOL,
             "error_if_validators": (
@@ -3427,8 +3444,8 @@
             "build_fcn": (
                 build_binary_broadcast,
                 TosaTensorGen.tgBroadcastFuzz,
-                TosaTensorValuesGen.tvgDefault,
-                None,
+                TosaTensorValuesGen.tvgLazyGenDefault,
+                TosaArgGen.agNone,
             ),
             "types": TYPE_FI32,
             "error_if_validators": (
@@ -3440,6 +3457,9 @@
                 TosaErrorValidator.evDimensionMismatch,
                 TosaErrorValidator.evBroadcastShapesMismatch,
             ),
+            "data_gen": {
+                "fp": (gtu.DataGenType.PSEUDO_RANDOM,),
+            },
         },
         "minimum": {
             "op": Op.MINIMUM,
@@ -3447,8 +3467,8 @@
             "build_fcn": (
                 build_binary_broadcast,
                 TosaTensorGen.tgBroadcastFuzz,
-                TosaTensorValuesGen.tvgDefault,
-                None,
+                TosaTensorValuesGen.tvgLazyGenDefault,
+                TosaArgGen.agNone,
             ),
             "types": TYPE_FI32,
             "error_if_validators": (
@@ -3460,6 +3480,9 @@
                 TosaErrorValidator.evDimensionMismatch,
                 TosaErrorValidator.evBroadcastShapesMismatch,
             ),
+            "data_gen": {
+                "fp": (gtu.DataGenType.PSEUDO_RANDOM,),
+            },
         },
         "mul": {
             "op": Op.MUL,
@@ -3491,8 +3514,8 @@
             "build_fcn": (
                 build_binary_broadcast,
                 TosaTensorGen.tgBroadcastFuzz,
-                TosaTensorValuesGen.tvgDefault,
-                None,
+                TosaTensorValuesGen.tvgLazyGenDefault,
+                TosaArgGen.agNone,
             ),
             "types": TYPE_FP,
             "error_if_validators": (
@@ -3512,7 +3535,7 @@
                 build_binary_broadcast,
                 TosaTensorGen.tgBroadcastFuzz,
                 TosaTensorValuesGen.tvgAddSub,
-                None,
+                TosaArgGen.agNone,
             ),
             "types": TYPE_FI32,
             "error_if_validators": (
@@ -3524,6 +3547,10 @@
                 TosaErrorValidator.evDimensionMismatch,
                 TosaErrorValidator.evBroadcastShapesMismatch,
             ),
+            "data_gen": {
+                "fp": (gtu.DataGenType.PSEUDO_RANDOM,),
+            },
+            "compliance": {"ulp": 0.5},
         },
         "table": {
             "op": Op.TABLE,
@@ -4379,7 +4406,7 @@
                 build_rfft2d,
                 TosaTensorGen.tgRFFT2d,
                 TosaTensorValuesGen.tvgDefault,
-                TosaArgGen.agNone,
+                None,
             ),
             "types": [DType.FP32],
             "error_if_validators": (
diff --git a/verif/generator/tosa_verif_build_tests.py b/verif/generator/tosa_verif_build_tests.py
index d6598fb..d01e8a7 100644
--- a/verif/generator/tosa_verif_build_tests.py
+++ b/verif/generator/tosa_verif_build_tests.py
@@ -301,32 +301,42 @@
     results = []
     for test_type in testType:
         testList = []
-        for op in ttg.TOSA_OP_LIST:
-            if re.match(args.filter + ".*", op):
-                testList.extend(
-                    ttg.genOpTestList(
-                        op,
-                        shapeFilter=args.target_shapes,
-                        rankFilter=args.target_ranks,
-                        dtypeFilter=args.target_dtypes,
-                        testType=test_type,
+        try:
+            for opName in ttg.TOSA_OP_LIST:
+                if re.match(args.filter + ".*", opName):
+                    testList.extend(
+                        ttg.genOpTestList(
+                            opName,
+                            shapeFilter=args.target_shapes,
+                            rankFilter=args.target_ranks,
+                            dtypeFilter=args.target_dtypes,
+                            testType=test_type,
+                        )
                     )
-                )
+        except Exception as e:
+            print(f"INTERNAL ERROR: Failure generating test lists for {opName}")
+            raise e
 
         print("{} matching {} tests".format(len(testList), test_type))
 
         testStrings = []
-        for opName, testStr, dtype, error, shapeList, testArgs in testList:
-            # Check for and skip duplicate tests
-            if testStr in testStrings:
-                print(f"Skipping duplicate test: {testStr}")
-                continue
-            else:
-                testStrings.append(testStr)
+        try:
+            for opName, testStr, dtype, error, shapeList, testArgs in testList:
+                # Check for and skip duplicate tests
+                if testStr in testStrings:
+                    print(f"Skipping duplicate test: {testStr}")
+                    continue
+                else:
+                    testStrings.append(testStr)
 
-            results.append(
-                ttg.serializeTest(opName, testStr, dtype, error, shapeList, testArgs)
-            )
+                results.append(
+                    ttg.serializeTest(
+                        opName, testStr, dtype, error, shapeList, testArgs
+                    )
+                )
+        except Exception as e:
+            print(f"INTERNAL ERROR: Failure creating test output for {opName}")
+            raise e
 
     print(f"Done creating {len(results)} tests")