update

app.py

@@ -857,22 +857,7 @@ with gr.Blocks(theme=theme, css=custom_css) as demo:
 
     gr.Markdown("""
     <div style="font-size: 18px;">          
-    AttnTrace is an efficient context traceback method for long contexts (e.g., full papers). It is over 15× faster than the state-of-the-art context traceback method TracLLM. Compared to previous attention-based approaches, AttnTrace is more accurate, reliable, and memory-efficient.
-    """, elem_classes="feature-highlights")
-    
-    # Image
-    with gr.Row():
-        with gr.Column(scale=3):
-            pass
-        with gr.Column(scale=4):
-            gr.Image("assets/fig1.png", show_label=False, container=False)
-        with gr.Column(scale=3):
-            pass
-    
-    # Feature highlights
-    gr.Markdown("""
-    <div style="font-size: 18px;">    
-    As shown in the above figure, AttnTrace can trace back to the texts in a long context that contribute to the output of an LLM. AttnTrace can be used in many real-world applications, such as tracing back to: 
+    AttnTrace is an efficient context traceback method for long contexts (e.g., full papers). It is over 15× faster than the state-of-the-art context traceback method TracLLM. Compared to previous attention-based approaches, AttnTrace is more accurate, reliable, and memory-efficient. AttnTrace can be used in many real-world applications, such as tracing back to: 
     
     - 📄 prompt injection instructions that manipulate LLM-generated paper reviews.
     - 💻 malicious comment & code hiding in the codebase that misleads the AI coding assistant.