Dimensions overall score 8.0

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• SkinFlow achieves a +12.06% gain in Top-1 accuracy on the Fitzpatrick17k benchmark compared to larger general-purpose models like Qwen3VL-235B and GPT-5.2.result 95%
• SkinFlow achieves a +28.57% boost in Top-6 accuracy on the Fitzpatrick17k benchmark compared to larger general-purpose models.result 95%
• SkinFlow's 7B model establishes a new state-of-the-art on the Fitzpatrick17k benchmark.result 90%
• SkinFlow utilizes a Virtual-Width Dynamic Vision Encoder (DVE) to 'unfold' complex pathological manifolds without physical parameter expansion.technical 90%
• SkinFlow employs a two-stage Reinforcement Learning strategy that sequentially aligns explicit medical descriptions and reconstructs implicit diagnostic textures.method 90%
• General-purpose Large Vision-Language Models (LVLMs) often falter in dermatology due to 'diffuse attention' - the inability to disentangle subtle pathological lesions from background noise.technical 85%
• SkinFlow's performance may vary across different skin types and conditions not covered in the training data.limitation 80%
• Optimizing geometric capacity and information flow yields superior diagnostic reasoning compared to raw parameter scaling.technical 85%

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