[Docs] update documents

docs/.nav.yml

@@ -107,7 +107,7 @@ nav:
       - design/feature/hsdp.md
       - design/feature/cache_dit.md
       - design/feature/teacache.md
-      - design/feature/async_chunk_design.md
+      - design/feature/async_chunk.md
       - design/feature/vae_parallel.md
       - design/feature/diffusion_step_execution.md
     - Module Design:

docs/contributing/model/adding_omni_model.md

@@ -408,18 +408,17 @@ Understanding the data structures is crucial for implementing stage transitions:
 
 **Input to your function:**
 - `stage_list[source_stage_id].engine_outputs`: List of `EngineCoreOutput` objects
-  - Each contains `outputs`: List of `RequestOutput` objects
-  - Each `RequestOutput` has:
-    - `token_ids`: Generated token IDs
-    - `multimodal_output`: Dict with keys like `"code_predictor_codes"`, etc.
-      - These are the hidden states or intermediate outputs from the model's forward pass
-    - `prompt_token_ids`: Original prompt token IDs
+-   -  Each contains `outputs`: List of `RequestOutput` objects
+    - Each `RequestOutput` has:
+-   -  - `token_ids`: Generated token IDs
+       - `multimodal_output`: Dict with keys like `"code_predictor_codes"`, etc.These are the hidden states or intermediate outputs from the model's forward pass
+       - `prompt_token_ids`: Original prompt token IDs
 
 **Output from your function:**
 - Must return `list[OmniTokensPrompt]` where each `OmniTokensPrompt` contains:
-  - `prompt_token_ids`: List[int] - Token IDs for the next stage
-  - `additional_information`: Dict[str, Any] - Optional metadata (e.g., embeddings, hidden states)
-  - `multi_modal_data`: Optional multimodal data if needed
+-   - `prompt_token_ids`: List[int] - Token IDs for the next stage
+    - `additional_information`: Dict[str, Any] - Optional metadata (e.g., embeddings, hidden states)
+    - `multi_modal_data`: Optional multimodal data if needed
 
 ### How Model Outputs Are Stored
 

docs/contributing/model/adding_tts_model.md

@@ -28,7 +28,7 @@ and can be placed on different devices. Qwen3-TTS has two stages:
 
 Each stage is a separate model class configured independently via YAML. The two stages
 are connected by the `async_chunk` framework, which enables inter-stage streaming for
-low first-packet latency (see [Async Chunk Design](../../design/feature/async_chunk_design.md)).
+low first-packet latency (see [Async Chunk Design](../../design/feature/async_chunk.md)).
 
 ### Without async_chunk (batch mode)
 
@@ -591,5 +591,5 @@ Adding a TTS model to vLLM-Omni involves:
 For more information, see:
 
 - [Architecture Overview](../../design/architecture_overview.md)
-- [Async Chunk Design](../../design/feature/async_chunk_design.md)
+- [Async Chunk Design](../../design/feature/async_chunk.md)
 - [Stage Configuration Guide](../../configuration/stage_configs.md)

docs/design/feature/async_chunk_design.md → docs/design/feature/async_chunk.md

@@ -1,4 +1,4 @@
-# Async Chunk Design
+# Async Chunk
 
 ## Table of Contents
 
@@ -88,8 +88,9 @@ The following diagram illustrates the **Async Chunk Architecture** for multi-sta
 </p>
 
 **Diagram Legend:**
+
 | Step | Stage Type | Description |
-|:------:|:-----------:|:------------|
+|------|-----------|------------|
 | `prefill` | Initialization | Context processing, KV cache initialization |
 | `decode` | Autoregressive | Token-by-token generation in AR stages |
 | `codes` | Audio Encoding | RVQ codec codes from Talker stage |