Upload Leaderboard.vue
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src/views/Leaderboard.vue
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@@ -51,6 +51,10 @@ const lastSelectedDataNameChart = ref('')
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// header markdown 内容
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const headerMarkdown = ref(`
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**Information Capacity** evaluates an LLM's **efficiency** based on text compression performance relative to computational complexity, harnessing the inherent correlation between **compression** and **intelligence**.
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Larger models can predict the next token more accurately, leading to higher compression gains but at increased computational costs.
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It also facilitates dynamic routing of different-sized models for efficient handling of tasks with varying difficulties, which is especially relevant to the device-edge-cloud infrastructure detailed in the **AI Flow** framework.
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With the rapid evolution of edge intelligence, we believe that this hierarchical network will replace the mainstream cloud-centric computing scheme in the near future.
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If you want to add your evaluation results to the leaderboard, please submit a PR at [our GitHub repo](https://github.com/TeleAI-AI-Flow/InformationCapacity).
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`)
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const title = 'Information Capacity Leaderboard'
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// header markdown 内容
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const headerMarkdown = ref(`
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<p align="center">
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🏆 <a href="https://huggingface.co/spaces/TeleAI-AI-Flow/InformationCapacityLeaderboard"> Leaderboard</a>    |   
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🖥️ <a href="https://github.com/TeleAI-AI-Flow/InformationCapacity">GitHub</a>    |    🤗 <a href="https://huggingface.co/datasets/TeleAI-AI-Flow/InformationCapacity">Hugging Face</a>   |    📑  <a href="https://www.arxiv.org/abs/2511.08066">Paper</a>
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</p>
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**Information Capacity** evaluates an LLM's **efficiency** based on text compression performance relative to computational complexity, harnessing the inherent correlation between **compression** and **intelligence**.
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Larger models can predict the next token more accurately, leading to higher compression gains but at increased computational costs.
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It also facilitates dynamic routing of different-sized models for efficient handling of tasks with varying difficulties, which is especially relevant to the device-edge-cloud infrastructure detailed in the **AI Flow** framework.
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With the rapid evolution of edge intelligence, we believe that this hierarchical network will replace the mainstream cloud-centric computing scheme in the near future.
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If you want to add your evaluation results to the leaderboard, please submit a PR at [our GitHub repo](https://github.com/TeleAI-AI-Flow/InformationCapacity).
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`)
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const title = 'Information Capacity Leaderboard'
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