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README.md

@@ -73,7 +73,7 @@ We conducted a comprehensive evaluation of **MOSS-VL-Instruct-0408** across four
 
 *   **🚀 Leading Video Intelligence**: MOSS-VL achieves a score of **65.8** in Video Understanding, significantly outperforming Qwen3-VL (+2pts). It shows exceptional temporal consistency and action recognition capabilities across benchmarks like `VideoMME`, `MLVU`, `EgoSchema`, and `VSI-bench` (where it outperforms **Qwen3-VL-8B-Instruct** by **8.3 points**).
 *   **👁️ Outstanding Multimodal Perception**: MOSS-VL delivers excellent general image-text understanding, shining in fine-grained object recognition and spatial reasoning on benchmarks like `BLINK` and `MMBench`.
-*   **🧠 Robust Multimodal Reasoning**: MOSS-VL demonstrates solid logical inference, staying highly competitive with the latest Qwen series on challenging reasoning suites such as `VisuLogic`.
+*   **🧠 Robust Multimodal Reasoning**: MOSS-VL demonstrates solid logical inference, staying highly competitive with the latest Qwen series on challenging reasoning suites.
 *   **📄 Reliable Document Understanding**: While the model is primarily optimized for general perception, MOSS-VL still delivers **83.9** on OCR and document analysis, ensuring dependable extraction of text and structured information.
 
 
@@ -82,9 +82,7 @@ We conducted a comprehensive evaluation of **MOSS-VL-Instruct-0408** across four
 </p>
 
 ## 🚀 Quickstart
-### 🛠️ Requirements
-
-Installation:
+### 🛠️ Installation
 
 ```bash
 conda create -n moss_vl python=3.12 pip -y
@@ -281,8 +279,7 @@ texts = [item["text"] for item in result["results"]]
 
 MOSS-VL-Instruct-0408 represents an early milestone in the MOSS-VL roadmap, and we're actively working on several directions to push it further:
 
-- 🧮 **Math & Code Reasoning** — While the current checkpoint already exhibits solid general reasoning, we plan to substantially strengthen its mathematical reasoning and code reasoning capabilities, especially in multimodal contexts.
-- ⚡ **Real-Time Streaming Variant** — The upcoming **MOSS-VL-RealTime** will extend MOSS-VL to low-latency, streaming video understanding, enabling interactive applications such as live video chat, real-time event detection, and online assistants.
+- 🧮 **Math & Code Reasoning** — While the current checkpoint already exhibits great general reasoning, we plan to substantially strengthen its mathematical reasoning and code reasoning capabilities, especially in multimodal contexts.
 - 🎯 **RL Post-Training** — We are working on a reinforcement learning post-training stage to further align the model with human preferences and to unlock stronger multi-step reasoning behaviors on top of the SFT foundation.