diff --git a/neuralnetworks/1.0/types.hal b/neuralnetworks/1.0/types.hal
index 537331b..54ed402 100644
--- a/neuralnetworks/1.0/types.hal
+++ b/neuralnetworks/1.0/types.hal
@@ -1003,21 +1003,6 @@
 };
 
 /**
- * A typed operation.
- */
-struct OperationTuple {
-    /**
-     * The type of operation.
-     */
-    OperationType operationType;
-
-    /**
-     * The input data type of operation.
-     */
-    OperandType operandType;
-};
-
-/**
  * Performance information for the reference workload.
  *
  * Used by a driver to report its performance characteristics.
@@ -1039,20 +1024,6 @@
  */
 struct Capabilities {
     /**
-     * A collection of typed operations supported by the driver.
-     */
-    vec<OperationTuple> supportedOperationTuples;
-
-    /**
-     * Indicates whether a driver caches its prepared model for reuse the next
-     * time the application begins. This is useful because the model may have
-     * been prepared in a previous run.
-     *
-     * True if caching is supported, false otherwise.
-     */
-    bool cachesCompilation;
-
-    /**
      * Driver performance when operating on float32 data.
      */
     PerformanceInfo float32Performance;
@@ -1144,9 +1115,9 @@
  */
 struct Operation {
     /**
-     * The tuple describing the operation type and input type.
+     * The operation type.
      */
-    OperationTuple opTuple;
+    OperationType type;
 
     /**
      * Describes the table that contains the indexes of the inputs of the
diff --git a/neuralnetworks/1.0/vts/functional/Models.cpp b/neuralnetworks/1.0/vts/functional/Models.cpp
index 9802f62..8ce4f25 100644
--- a/neuralnetworks/1.0/vts/functional/Models.cpp
+++ b/neuralnetworks/1.0/vts/functional/Models.cpp
@@ -78,9 +78,7 @@
     };
 
     const std::vector<Operation> operations = {{
-        .opTuple = {OperationType::ADD, OperandType::TENSOR_FLOAT32},
-        .inputs = {operand1, operand2, operand3},
-        .outputs = {operand4},
+        .type = OperationType::ADD, .inputs = {operand1, operand2, operand3}, .outputs = {operand4},
     }};
 
     const std::vector<uint32_t> inputIndexes = {operand1};
@@ -107,8 +105,7 @@
 // create first invalid model
 Model createInvalidTestModel1() {
     Model model = createValidTestModel();
-    model.operations[0].opTuple = {static_cast<OperationType>(0xDEADBEEF) /* INVALID */,
-                                   OperandType::TENSOR_FLOAT32};
+    model.operations[0].type = static_cast<OperationType>(0xDEADBEEF); /* INVALID */
     return model;
 }
 
diff --git a/neuralnetworks/1.0/vts/functional/VtsHalNeuralnetworksV1_0TargetTest.cpp b/neuralnetworks/1.0/vts/functional/VtsHalNeuralnetworksV1_0TargetTest.cpp
index 59d66ba..0f354d1 100644
--- a/neuralnetworks/1.0/vts/functional/VtsHalNeuralnetworksV1_0TargetTest.cpp
+++ b/neuralnetworks/1.0/vts/functional/VtsHalNeuralnetworksV1_0TargetTest.cpp
@@ -107,9 +107,6 @@
     Return<void> ret =
         device->getCapabilities([](ErrorStatus status, const Capabilities& capabilities) {
             EXPECT_EQ(ErrorStatus::NONE, status);
-            EXPECT_NE(nullptr, capabilities.supportedOperationTuples.data());
-            EXPECT_NE(0ull, capabilities.supportedOperationTuples.size());
-            EXPECT_EQ(0u, static_cast<uint32_t>(capabilities.cachesCompilation) & ~0x1);
             EXPECT_LT(0.0f, capabilities.float32Performance.execTime);
             EXPECT_LT(0.0f, capabilities.float32Performance.powerUsage);
             EXPECT_LT(0.0f, capabilities.quantized8Performance.execTime);