Update titles and improve clarity in LiteRT documentation

Author: madeline-underwood

content/learning-paths/mobile-graphics-and-gaming/litert-sme/1-litert-kleidiai-sme2.md

@@ -1,24 +1,20 @@
 ---
-title: Understand LiteRT, XNNPACK, KleidiAI and SME2
+title: Understand LiteRT, XNNPACK, KleidiAI, and SME2
 weight: 2
 
 ### FIXED, DO NOT MODIFY
 layout: learningpathall
 ---
 
-## LiteRT, XNNPACK, KleidiAI and SME2
+## LiteRT, XNNPACK, KleidiAI, and SME2
 
-LiteRT (Lite Runtime), formerly known as TensorFlow Lite, is a runtime for on-device AI.
-The default CPU acceleration library used by LiteRT is XNNPACK.
+LiteRT (Lite Runtime, formerly TensorFlow Lite) is a runtime for on-device AI on Arm platforms. The default CPU acceleration library used by LiteRT is XNNPACK (an open-source library providing highly optimized neural-network operators).
 
 XNNPACK is an open-source library that provides highly optimized implementations of neural-network operators. It continuously integrates KleidiAI library to leverage new CPU features such as SME2.
 
 KleidiAI is a library developed by Arm that offers performance-critical micro-kernels leveraging Arm architecture features, such as SME2.
 
-Both XNNPACK and KleidiAI are external dependencies of LiteRT. LiteRT specifies the versions of these libraries to use.
-When LiteRT is built with both XNNPACK and KleidiAI enabled, XNNPACK invokes KleidiAI’s micro-kernels at runtime to accelerate operators with supported data types; otherwise, it falls back to its own implementation.
-
-The software stack for LiteRT is as follows.
+The software stack for LiteRT is shown below.
 
 ![LiteRT, XNNPACK, KleidiAI and SME2#center](./litert-sw-stack.png "LiteRT, XNNPACK, KleidiAI and SME2")
 

content/learning-paths/mobile-graphics-and-gaming/litert-sme/_index.md

@@ -1,24 +1,19 @@
 ---
 title: Accelerate LiteRT Models on Android with KleidiAI and SME2
 
-draft: true
-cascade:
-    draft: true
-
 minutes_to_complete: 30
 
-who_is_this_for: This is an advanced topic for developers looking to leverage the Arm's Scalable Matrix Extension Version 2 (SME2) instructions to accelerate LiteRT models inference on Android.
+who_is_this_for: This is an advanced topic for developers looking to leverage Arm's Scalable Matrix Extension version 2 (SME2) instructions to accelerate LiteRT model inference on Android.
 
 learning_objectives: 
-    - Understand how KleidiAI works in LiteRT.
-    - Build the LiteRT benchmark tool and enable XNNPACK and KleidiAI with SME2 support in LiteRT.
-    - Create LiteRT models that can be acclerated by SME2 through KleidiAI.
-    - Use the benchmark tool to evaluate and validate the SME2 acceleration performance of LiteRT models.
-
+- Understand how KleidiAI integrates with LiteRT
+- Build the LiteRT benchmark tool and enable XNNPACK and KleidiAI with SME2 support in LiteRT
+- Create LiteRT models that can be accelerated by SME2 through KleidiAI
+- Use the benchmark tool to evaluate and validate the SME2 acceleration performance of LiteRT models
 
 prerequisites:
-    - An x86_64 Linux development machine.
-    - An Android device that supports the Arm SME2 architecture features - see the list of [devices with SME2 support](/learning-paths/cross-platform/multiplying-matrices-with-sme2/1-get-started/#devices)
+- An Arm64 Linux development machine 
+- An Android device that supports Arm SME2 architecture features - see the list of [devices with SME2 support](/learning-paths/cross-platform/multiplying-matrices-with-sme2/1-get-started/#devices)
 
 author: Jiaming Guo