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@@ -54,7 +54,6 @@ Built on top of MOSS-VL-Base-0408 through supervised fine-tuning (SFT), this che
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  <img src="assets/structure.png" alt="MOSS-VL Architecture" width="90%"/>
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  </p>
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  ## 🧩 Absolute Timestamps
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  To ensure the model accurately perceives the pacing and duration of events, **MOSS-VL-Instruct-0408** injects **absolute timestamps** alongside each sampled frame, grounding the reasoning process in a **precise temporal reference**.
@@ -71,7 +70,6 @@ MOSS-VL utilizes Cross-attention Rotary Position Embedding (XRoPE), tailored to
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  <img src="assets/3d-rope.png" alt="MOSS-VL mRoPE Architecture Illustration" width="80%"/>
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  </p>
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  ## 📊 Model Performance
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  We conducted a comprehensive evaluation of **MOSS-VL-Instruct-0408** across four key dimensions: Multimodal Perception, Document/OCR, Multimodal Reasoning, and Video Understanding. The results demonstrate that MOSS-VL achieves outstanding performance, particularly excelling in **general multimodal perception** and **complex video analysis**.
@@ -90,10 +88,7 @@ We conducted a comprehensive evaluation of **MOSS-VL-Instruct-0408** across four
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  <img src="assets/MOSS-VL-benchmark.png" alt="MOSS-VL Benchmark Results" width="100%"/>
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  </p>
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  ## 🚀 Quickstart
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  ### 🛠️ Requirements
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  Installation commands:
@@ -294,9 +289,6 @@ texts = [item["text"] for item in result["results"]]
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  </details>
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  ## 🚧 Limitations and Future Work
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  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:
 
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  <img src="assets/structure.png" alt="MOSS-VL Architecture" width="90%"/>
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  </p>
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  ## 🧩 Absolute Timestamps
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  To ensure the model accurately perceives the pacing and duration of events, **MOSS-VL-Instruct-0408** injects **absolute timestamps** alongside each sampled frame, grounding the reasoning process in a **precise temporal reference**.
 
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  <img src="assets/3d-rope.png" alt="MOSS-VL mRoPE Architecture Illustration" width="80%"/>
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  </p>
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  ## 📊 Model Performance
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  We conducted a comprehensive evaluation of **MOSS-VL-Instruct-0408** across four key dimensions: Multimodal Perception, Document/OCR, Multimodal Reasoning, and Video Understanding. The results demonstrate that MOSS-VL achieves outstanding performance, particularly excelling in **general multimodal perception** and **complex video analysis**.
 
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  <img src="assets/MOSS-VL-benchmark.png" alt="MOSS-VL Benchmark Results" width="100%"/>
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  </p>
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  ## 🚀 Quickstart
 
 
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  ### 🛠️ Requirements
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  Installation commands:
 
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  </details>
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  ## 🚧 Limitations and Future Work
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  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: