Rewrite the content, add AI use case to explain the idea.

Author: odinshen-git

content/learning-paths/cross-platform/multimodel_mnn_v9/1_mnn_v9.md

@@ -8,18 +8,20 @@ weight: 2
 
 This section introduces the software stack used throughout this Learning Path. You will use **[MNN](https://github.com/alibaba/MNN)** to run a prebuilt **Omni multimodal model** on an Armv9 Linux system using only the CPU.
 
-By the end of this section, you will understand why this combination is a practical starting point for reproducible multimodal inference on Armv9.
+By the end of this section, you will understand why this combination is a practical starting point for reproducible multimodal inference on Armv9, and use retail restocking workflow as an example combines local image and audio inputs use case.
 
 ## Why use MNN on Armv9
 
 MNN is a lightweight inference engine designed for deployment across mobile, embedded, and edge platforms. It is a good fit for this Learning Path for four reasons:
 
-- It provides a **portable runtime** that can be built and used across different device classes.
-- It supports a **CPU-first deployment flow**, which maps well to Armv9 development systems.
-- Native builds can take advantage of **Arm-specific optimizations** when they are enabled in the build.
-- The same runtime approach can be reused across **Arm Linux, Android, iOS, and x86-based development hosts**.
+- It provides a **portable runtime** that can be built and reused across different eevice classes.
+- It supports a **CPU-first deployment flow**, which is useful when you want to validate multimodal inference on Armv9 without depending on a discrete GPU or dedicated accelerator.
+- Native builds can take advantage of **Armv9-specific CPU features and optimizations** when they are enabled in the build, making this a practical path for efficient local inference.
+- The same runtime approach can be reused across **Arm Linux, Android, iOS, and x86-based development hosts**, which improves portability from development to deployment.
 
-For this Learning Path, MNN gives you a practical way to validate multimodal inference on Armv9 without introducing extra framework complexity.
+For this Learning Path, MNN gives you a practical way to build a reproducible multimodal inference workflow on Armv9 while keeping the software stack compact and deployment-oriented.
+
+A retail restocking workflow is also a good use case example for Arm because it benefits from local CPU execution, simple deployment, and the ability to process store inputs close to where they are captured.
 
 ## Why use an Omni multimodal model
 
@@ -51,4 +53,4 @@ This scope keeps the focus on setup, validation, and multimodal application flow
 
 ## Next steps
 
-In the next section, you will build MNN on Armv9 and prepare the model files and local assets used in the remaining examples.
+In the next section, you will build MNN on Armv9 and prepare the model files and local assets used in the remaining examples.

content/learning-paths/cross-platform/multimodel_mnn_v9/2_mnn_build.md

@@ -8,7 +8,7 @@ layout: learningpathall
 
 In this module you will build **MNN** natively on your Arm v9 Linux system and verify that the `llm_demo` binary can load a **prebuilt Omni MNN model package**. This sets up everything needed for the text, vision, and audio demos in later modules.
 
-This creates the software environment used in the next modules, where you run text, vision, and audio-enabled multimodal inference on Armv9.
+This module uses a native **CPU-only** MNN build on Armv9. That is a deliberate design choice, not a fallback. The goal is to show how a compact, reproducible, deployment-friendly software stack can run directly on an Armv9 CPU without depending on a discrete GPU or separate accelerator.
 
 At the end of this module, you will have:
 
@@ -25,6 +25,12 @@ mkdir -p ~/mnn
 cd ~/mnn
 ```
 
+## Why build natively on Armv9 first
+
+This Learning Path uses a native build on the target Armv9 system before introducing any cross-compilation workflow. Building directly on the target helps reduce environment drift, makes library and toolchain issues easier to diagnose, and lets you validate the runtime in the same environment where you will execute the model.
+
+A native-first approach also makes it easier to confirm that the final binary, shared libraries, and model assets work together correctly on the Armv9 platform. For a reproducible Learning Path, this is usually the fastest way to get to a working baseline before optimizing or automating the workflow further.
+
 ## Build MNN natively on Armv9
 
 Clone the MNN repository:
@@ -58,14 +64,19 @@ The most important CMake options are:
 - `MNN_BUILD_AUDIO=ON` to enable the audio components used by the Omni model
 - `MNN_BUILD_LLM_OMNI=ON` to enable multimodal Omni support
 - `MNN_LOW_MEMORY=ON` to prefer lower-memory runtime settings where available
-- `MNN_KLEIDIAI=ON` to enable KleidiAI optimizations on supported Arm platforms
+- `MNN_KLEIDIAI=ON` to enable Arm-specific optimizations through KleidiAI
+
+Among these options, `MNN_KLEIDIAI=ON` is the most important Arm-specific build flag in this workflow. It enables Arm-focused optimizations through KleidiAI, making it especially relevant when you want to validate efficient local inference on Armv9 CPUs.
+
+In this Learning Path, a CPU-first build keeps the setup simpler, reduces external dependencies, and makes the resulting workflow easier to reproduce across edge and embedded Arm systems.
 
 Verify that the `llm_demo` binary was created:
 
 ```bash
 ls -l ~/mnn/MNN/build/llm_demo
 ```
 
+
 ## Verify shared library resolution
 
 If your system already has another MNN installation, `llm_demo` can load a different `libMNN.so` than the one you just built. This is a common source of runtime errors.

content/learning-paths/cross-platform/multimodel_mnn_v9/3_mnn_text_baseline.md

@@ -6,17 +6,19 @@ layout: learningpathall
 
 ## Introduction
 
-Before you add image and audio inputs, validate the basic inference path with a text-only prompt. In this module, you use the same **Qwen2.5-Omni-7B-MNN** package from the previous section and confirm that `llm_demo` can run prompts from a file on your Armv9 system.
+In this section, you run a **text-only baseline** using the Omni model on an Armv9 Linux system. Before adding image and audio inputs, this baseline helps you confirm that the core inference path is working correctly with a simple prompt and predictable output behavior.
 
-At the end of this module, you will have verified:
+This text-only baseline serves an important purpose in the Learning Path. Before adding image and audio inputs, it lets you verify the Armv9 runtime, basic token generation, and feature detection in isolation. That makes it easier to confirm that the core inference path is working correctly before you introduce additional multimodal variables.
 
-- the model configuration loads correctly
-- prompt input from a text file is parsed as expected
-- the runtime returns usable text output without crashes or library errors
+By the end of this module, you will be able to:
 
-## Create a text baseline prompt
+- run a reproducible text-only inference baseline on an Armv9 CPU
+- verify that the MNN runtime, prompt input path, and token generation are working correctly
+- establish a text baseline that you can use to compare later vision, audio, and multimodal runs
 
-Create a short prompt file in your workspace:
+## Create a baseline prompt
+
+Create a small prompt file in your workspace:
 
 ```bash
 cat > ~/mnn/text_baseline_prompt.txt <<'EOF'

content/learning-paths/cross-platform/multimodel_mnn_v9/4_mnn_vision.md

@@ -6,7 +6,7 @@ layout: learningpathall
 
 ## Introduction
 
-In this module, you use a local image and an ***MNN Omni multimodal model*** to run a simple retail shelf audit on an ***Armv9*** system.
+This section shows how Armv9 can run lightweight operational vision reasoning locally without requiring cloud round trips, making it a practical fit for store-side restocking workflows.
 
 The goal is not to produce SKU-level counting. Instead, you generate an operational signal that is useful for store staff:
 
@@ -17,6 +17,8 @@ The goal is not to produce SKU-level counting. Instead, you generate an operatio
 
 This keeps the task practical for on-device multimodal inference. In many retail workflows, deciding **where to restock first** is more useful than attempting perfect item counting from a single photo.
 
+By the end of this section, you will produce a structured shelf audit signal from a local image and verify that it can guide a restocking decision
+
 ## Prepare the image asset
 
 Create a local directory for assets:
@@ -93,6 +95,12 @@ If you have any other questions about this shelf auditing, feel free to let me k
 
 The exact wording can vary, but the response should still follow the requested structure.
 
+## Why a coarse coverage estimate is enough for edge workflows
+
+In many edge retail workflows, you do not need a perfectly precise inventory measurement to make a useful decision. A coarse estimate such as `High`, `Medium`, or `Low` coverage is often enough to identify which shelf area needs attention first and to generate a practical restocking signal.
+
+This keeps the prompt and output simple, reduces post-processing requirements, and makes the result easier to validate during early on-device prototyping. For this Learning Path, the goal is not to build a full inventory counting system, but to show how local vision reasoning on Armv9 can produce an operationally useful input for a restocking workflow.
+
 ## Verify the result
 
 Check that the output meets these conditions:
@@ -108,4 +116,4 @@ If the model returns extra conversational text, tighten the prompt further by re
 
 You have now validated image-based multimodal inference with MNN Omni on Armv9.
 
-In the next module, you extend this workflow by running an audio-based restock instruction demo using an `<audio>...</audio>` prompt.
+In the next section, you extend this workflow by running an audio-based restock instruction demo using an `<audio>...</audio>` prompt.

content/learning-paths/cross-platform/multimodel_mnn_v9/5_mnn_vision_audio.md

@@ -57,6 +57,10 @@ The spoken content can be similar to:
 This is the pet food aisle, left shelf. Please restock the bottom left large pet food bags, add 10 bags. Also restock the middle left canned food, add 24 cans. Finish before 3 PM today. If something is out of stock, use a similar substitute.
 ```
 
+In store inspection scenarios, unreliable connectivity, or environments where teams prefer not to upload audio in real time, local speech understanding on Armv9 has clear deployment value.
+
+In this module, the audio input is not just an audio prompt. It acts as on-device spoken task capture for store operations, turning a short spoken restocking note into structured operational input.
+
 ## Create the audio prompt
 
 Create a prompt file that attaches the WAV file and asks the model to return a single structured line.
@@ -120,15 +124,15 @@ The exact wording can vary. What matters is that the response stays close to the
 
 ## Verify the result
 
-Check that:
+A successful run should show that:
 
-- the quantities and deadline match the spoken note
-- any missing field is returned as `NOT_SURE`
-- the output remains on a single structured line
-- the ticket is readable enough for a simple downstream workflow
+- the model can process the local audio file on Armv9
+- the response keeps the requested ticket structure
+- explicit spoken values such as quantity or deadline appear in the output
+- missing or unclear fields are marked as `NOT_SURE` rather than invented
 
-If the model adds extra commentary after the ticket, tighten the prompt and rerun the command. If quantities or zones are wrong, verify the audio clarity and confirm that the WAV file was converted correctly.
+At this stage, the goal is not to evaluate full speech recognition quality. The goal is to validate that local audio understanding can extract operational task information that you can combine with other inputs later.
 
 ## Next steps
 
-In the next module, you will combine multimodal inference patterns into a practical restock workflow on Armv9.
+In the next section, you will combine multimodal inference patterns into a practical restock workflow on Armv9.

content/learning-paths/cross-platform/multimodel_mnn_v9/6_mnn_restock_ticket.md

@@ -6,7 +6,9 @@ layout: learningpathall
 
 ## Create the single-shot multimodal prompt (image + audio)
 
-In this final module, you combine image and audio inputs into a single multimodal prompt and generate one operational restock ticket on an Armv9 system.
+In this final section, you combine image and audio inputs into a single multimodal prompt and generate one operational restock ticket on an Armv9 system.
+
+This section demonstrates end-to-end multimodal task synthesis on an Armv9 CPU. Instead of testing a single modality in isolation, you combine image and audio inputs to produce one structured restocking ticket that can support a store action.
 
 You will combine:
 - A **shelf image** to estimate shelf coverage and select a priority zone

content/learning-paths/cross-platform/multimodel_mnn_v9/_index.md

@@ -1,5 +1,5 @@
 ---
-title: Multimodal On-Device Inference on Armv9 with MNN for Audio and Vision
+title: Build a Multimodal Retail Restocking Assistant on Armv9 With MNN
 
 minutes_to_complete: 60