diff --git a/current.txt b/current.txt
index 078a9dfde4..1f322638ca 100644
--- a/current.txt
+++ b/current.txt
@@ -401,7 +401,7 @@ f7d7cb747dc01a9fdb2d39a80003b4d8df9be733d65f5842198802eb6209db69 android.hardwar
 65a021fa89085b62fc96b2b6d3bef2f9103cf4d63379c68bc154fd9eef672852 android.hardware.health@1.0::types
 b7ecf29927055ec422ec44bf776223f07d79ad9f92ccf9becf167e62c2607e7a android.hardware.keymaster@4.0::IKeymasterDevice
 574e8f1499436fb4075894dcae0b36682427956ecb114f17f1fe22d116a83c6b android.hardware.neuralnetworks@1.0::IPreparedModel
-e75759b40a1c5f97b463b30aab91954012c9ea9e454dde308db853a56796e5a6 android.hardware.neuralnetworks@1.0::types
+1e3576c07006d82ba5bc6ddbf87c101414d361c41afe7a82713750844c488725 android.hardware.neuralnetworks@1.0::types
 eb754b58c93e5591613208b4c972811288b0fa16a82430d602f107c91a908b22 android.hardware.neuralnetworks@1.1::types
 1d4a5776614c08b5d794a5ec5ab04697260cbd4b3441d5935cd53ee71d19da02 android.hardware.radio@1.0::IRadioResponse
 ed9da80ec0c96991fd03f0a46107815d0e50f764656e49dba4980fa5c31d5bc3 android.hardware.radio@1.0::types
@@ -515,7 +515,7 @@ b83317b66721241887d2770b5ae95fd5af1e77c5daa7530ecb08fae8892f2b43 android.hardwar
 92714960d1a53fc2ec557302b41c7cc93d2636d8364a44bd0f85be0c92927ff8 android.hardware.neuralnetworks@1.2::IExecutionCallback
 36e1064c869965dee533c537cefbe87e54db8bd8cd45be7e0e93e00e8a43863a android.hardware.neuralnetworks@1.2::IPreparedModel
 e1c734d1545e1a4ae749ff1dd9704a8e594c59aea7c8363159dc258e93e0df3b android.hardware.neuralnetworks@1.2::IPreparedModelCallback
-e3b6176e3bf235c4e0e4e451b0166e396c7ee176cfe167c9147c3d46d7b34f0c android.hardware.neuralnetworks@1.2::types
+d18bba0b6c8d2d1da3cfb52b14f556d2e04eb91551d97ee60a3524cf993a3e0e android.hardware.neuralnetworks@1.2::types
 cf7a4ba516a638f9b82a249c91fb603042c2d9ca43fd5aad9cf6c0401ed2a5d7 android.hardware.nfc@1.2::INfc
 abf98c2ae08bf765db54edc8068e36d52eb558cff6706b6fd7c18c65a1f3fc18 android.hardware.nfc@1.2::types
 4cb252dc6372a874aef666b92a6e9529915aa187521a700f0789065c3c702ead android.hardware.power.stats@1.0::IPowerStats
diff --git a/neuralnetworks/1.0/types.hal b/neuralnetworks/1.0/types.hal
index b0a1c1aec2..02db063e18 100644
--- a/neuralnetworks/1.0/types.hal
+++ b/neuralnetworks/1.0/types.hal
@@ -858,20 +858,21 @@ enum OperationType : int32_t {
      *   elements of the input matrices.
      *
      * The operation has the following independently optional inputs:
+     * * The cell-to-input weights (\f$W_{ci}\f$), cell-to-forget weights
+     *   (\f$W_{cf}\f$) and cell-to-output weights (\f$W_{co}\f$) either all
+     *   have values or neither of them have values (i.e., all set to null). If
+     *   they have values, the peephole optimization is used.
      * * The input-to-input weights (\f$W_{xi}\f$), recurrent-to-input weights
-     *   (\f$W_{hi}\f$), cell-to-input (\f$W_{ci}\f$) weights, and input gate
-     *   bias (\f$b_i\f$) either all have values, or none of them have values
-     *   (i.e., all set to null). If they have no values, coupling of input and
-     *   forget gates (CIFG) is used, in which case the input gate (\f$i_t\f$)
-     *   is calculated using the following equation instead.
+     *   (\f$W_{hi}\f$) and input gate bias (\f$b_i\f$) either all have values,
+     *   or none of them have values. If they have no values, coupling of input
+     *   and forget gates (CIFG) is used, in which case the input gate
+     *   (\f$i_t\f$) is calculated using the following equation instead.
      *   \f{eqnarray*}{
      *   i_t = 1 - f_t
      *   \f}
-     * * The cell-to-forget weights (\f$W_{cf}\f$) and cell-to-output weights
-     *   (\f$W_{co}\f$) either both have values or neither of them have values.
-     *   If they have values, the peephole optimization is used. Additionally,
-     *   if CIFG is not used, cell-to-input weights (\f$W_{ci}\f$) is also
-     *   required to have values for peephole optimization.
+     *   In case peephole optimization is used and CIFG is not used
+     *   cell-to-input (\f$W_{ci}\f$) weights must be present. Otherwise, the
+     *   cell-to-input weights must have no value.
      * * The projection weights (\f$W_{proj}\f$) is required only for the
      *   recurrent projection layer, and should otherwise have no value.
      * * The projection bias (\f$b_{proj}\f$) may (but not required to) have a
@@ -984,8 +985,8 @@ enum OperationType : int32_t {
      * Outputs:
      * * 0: The scratch buffer.
      *      A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
-     *      [batch_size, num_units * 4] with CIFG, or
-     *      [batch_size, num_units * 3] without CIFG.
+     *      [batch_size, num_units * 3] with CIFG, or
+     *      [batch_size, num_units * 4] without CIFG.
      * * 1: The output state (out) (\f$h_t\f$).
      *      A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
      *      [batch_size, output_size].
diff --git a/neuralnetworks/1.2/types.hal b/neuralnetworks/1.2/types.hal
index c2e8f22052..f368ce2cea 100644
--- a/neuralnetworks/1.2/types.hal
+++ b/neuralnetworks/1.2/types.hal
@@ -1177,20 +1177,21 @@ enum OperationType : int32_t {
      * https://arxiv.org/pdf/1607.06450.pdf
      *
      * The operation has the following independently optional inputs:
+     * * The cell-to-input weights (\f$W_{ci}\f$), cell-to-forget weights
+     *   (\f$W_{cf}\f$) and cell-to-output weights (\f$W_{co}\f$) either all
+     *   have values or neither of them have values (i.e., all set to null). If
+     *   they have values, the peephole optimization is used.
      * * The input-to-input weights (\f$W_{xi}\f$), recurrent-to-input weights
-     *   (\f$W_{hi}\f$), cell-to-input (\f$W_{ci}\f$) weights, and input gate
-     *   bias (\f$b_i\f$) either all have values, or none of them have values
-     *   (i.e., all set to null). If they have no values, coupling of input and
-     *   forget gates (CIFG) is used, in which case the input gate (\f$i_t\f$)
-     *   is calculated using the following equation instead.
+     *   (\f$W_{hi}\f$) and input gate bias (\f$b_i\f$) either all have values,
+     *   or none of them have values. If they have no values, coupling of input
+     *   and forget gates (CIFG) is used, in which case the input gate
+     *   (\f$i_t\f$) is calculated using the following equation instead.
      *   \f{eqnarray*}{
      *   i_t = 1 - f_t
      *   \f}
-     * * The cell-to-forget weights (\f$W_{cf}\f$) and cell-to-output weights
-     *   (\f$W_{co}\f$) either both have values or neither of them have values.
-     *   If they have values, the peephole optimization is used. Additionally,
-     *   if CIFG is not used, cell-to-input weights (\f$W_{ci}\f$) is also
-     *   required to have values for peephole optimization.
+     *   In case peephole optimization is used and CIFG is not used
+     *   cell-to-input (\f$W_{ci}\f$) weights must be present. Otherwise, the
+     *   cell-to-input weights must have no value.
      * * The projection weights (\f$W_{proj}\f$) is required only for the
      *   recurrent projection layer, and should otherwise have no value.
      * * The projection bias (\f$b_{proj}\f$) may (but not required to) have a