update README

Author: YihengWang

README.md

@@ -16,10 +16,13 @@ A unified evaluation toolkit and leaderboard for rigorously assessing the scient
 <img src="assets/icon/welcome.png" alt="welcome" height="24" style="vertical-align:middle;" />
 &nbsp;Welcome to the official repository of <strong>SciEval</strong>!
 
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+  <img src="assets/SciEvalKit.png" alt="SciEval capability radar" width="90%">
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-## <img src="assets/icon/why.png" alt="why" height="28" style="vertical-align:middle;" />&nbsp;Why SciEval?
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+## <img src="assets/icon/why.png" alt="why" height="28" style="vertical-align:middle;" />&nbsp;Why SciEval?
 
 **SciEval** is an open‑source evaluation framework and leaderboard aimed at measuring the **scientific intelligence** of large language and vision–language models.  
 Although modern frontier models often achieve *~90* on general‑purpose benchmarks, their performance drops sharply on rigorous, domain‑specific scientific tasks—revealing a persistent **general‑versus‑scientific gap** that motivates the need for SciEval.
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 - **Capability‑oriented & reproducible ▸** A unified toolkit for **dataset construction, prompt engineering, inference, and expert‑aligned scoring** ensures transparent and repeatable comparisons.  
 - **Grounded in real scenarios ▸** Benchmarks use domain‑specific data and tasks so performance reflects **actual scientific practice**, not synthetic proxies.
 
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-  <img src="assets/github.png" alt="SciEval capability radar" width="100%">
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 ## <img src="assets/icon/progress.png" alt="progress" height="28" style="vertical-align:middle;" />&nbsp;Progress in Scientific Intelligence
 
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 ## <img src="assets/icon/key.png" alt="key" height="28" style="vertical-align:middle;" />&nbsp;Key Features
 
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+  <img src="assets/radar.png" alt="SciEval capability radar" width="70%">
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 | Category                                  | Highlights                                                                                                                                                       |
 | ----------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------- |
 | **Seven Core Dimensions**            | Scientific Knowledge Understanding, Scientific Code Generation, Scientific Symbolic Reasoning, Scientific Hypothesis Generation, Scientific Multimodal Perception, Scientific Multimodal Reasoning, Scientific Multimodal Understanding |
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   <img src="assets/framework.png" alt="SciEval framework overview" width="65%">
 </div>
 
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   <em>
   An overview of the SciEval framework, illustrating how heterogeneous scientific datasets, unified prompt construction, model inference, and capability-oriented evaluators are integrated into a single reproducible evaluation pipeline.
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