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@@ -141,16 +141,6 @@ <h1 class="title is-1 publication-title">Ensembling Prioritized Hybrid Policies
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-  <!-- Title Image -->
-  <section class="hero teaser">
-    <div class="container is-max-desktop">
-      <div class="hero-body">
-        <img src="static/images/Overview.svg" alt="EPH Overview" width="960px"/>
-      </div>
-    </div>
-  </section>
-  <!-- End teaser video -->
    
 
 <!-- Paper abstract -->
@@ -188,10 +178,12 @@ <h2 class="title is-3">Abstract</h2>
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+  
 <section class="hero contributions">
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-    <h2 class="title is-3 is-3-light">Contributions</h2>
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+      <h2 class="title is-3 is-3-light">Contributions</h2>
+      <img src="static/images/Overview.svg" alt="EPH Overview" width="960px"/>
       <p>We propose <strong>EPH</strong> (<em>Ensembling Prioritized Hybrid Policies</em>), a Q-learning-based MARL-MAPF solver with communication. Our key contributions include:</p>
       <ol>
         <li><strong>Selective Communication</strong>: Enhanced communication block using Transformer-inspired improvements for better information extraction.</li>
@@ -205,8 +197,8 @@ <h2 class="title is-3 is-3-light">Contributions</h2>
 
 <section class="hero method is-light">
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-    <h2 class="title is-3 is-3-light">EPH</h2>
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+      <h2 class="title is-3 is-3-light">EPH</h2>
       <p><strong>EPH</strong>. EPH can be divided into two parts, as shown in the above picture. The upper part shows the neural network structure of EPH, and how local partial observations are transformed into Q vectors. The lower part shows that instead of getting action by directly applying \( a^t_i = \text{argmax} (q^t_i) \), several inference techniques, as mentioned in the <em>Contributions</em>, can be used to improve actions quality and avoid collisions.</p>
 
       <p><strong>Training Method</strong>. The Q value for agent \(i\) is obtained via:</p>
@@ -240,8 +232,8 @@ <h2 class="title is-3 is-3-light">EPH</h2>
 
 <section class="hero inference-techniques">
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-    <h2 class="title is-3 is-3-light">Inference Techniques</h2>
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+      <h2 class="title is-3 is-3-light">Inference Techniques</h2>
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         <div class="column is-half">
           <p><strong><em>Hybrid Expert Guidance</em></strong></p>
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-    <h2 class="title is-3 is-3-light">Experimental Results</h2>
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+      <h2 class="title is-3 is-3-light">Experimental Results</h2>
       <div class="columns">
         <div class="column is-half">
           <img src="static/images/success_rate.svg" alt="Success Rate" style="width:100%;">
@@ -327,9 +319,12 @@ <h2 class="title is-3">Videos</h2>
 
 
 <!-- BibText citation + copy to clipboard -->
-<section class="section" id="Code">
-  <div class="container is-max-desktop content">
+<section class="hero is-light" id="Code">
+  <div class="hero-body">
+    <div class="container">
+    <br>
     <h2 class="title">BibTeX</h2>
+    If you find this work useful, please consider citing it. We will appreciate it!
     <div class="code-container">
       <pre><code class="code-content language-text">@inproceedings{tang2024eph,
         title={Ensembling Prioritized Hybrid Policies for Multi-agent Pathfinding},
@@ -350,18 +345,48 @@ <h2 class="title">BibTeX</h2>
       <div class="copy-tooltip">Copy to clipboard</div>
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+  <br>
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 </section>
     
 </section>
 <!--End BibTex citation -->
 
 
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@@ -369,12 +394,11 @@ <h2 class="title">BibTeX</h2>
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-    border: 1px solid #f3f3f3;
+    border: 1px solid #000000;
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+.logos-section {
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+  background-color: #ffffff; /* Optional: Add background color for the section */
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+.logos-container {
+  display: flex;
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+}
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+  flex: 1;
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+  padding: 0 1em;
+}
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+.logo-item img {
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+  max-width: 100%;
+  object-fit: contain;
+  transition: transform 0.3s ease, filter 0.3s ease; /* Smooth transition for transform and shadow */
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+}
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+/* Hover effect with pop-up and shadow */
+.logo-item:hover img {
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+/* Optional: Press effect on click */
+.logo-item:active img {
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+  filter: drop-shadow(0px 4px 8px rgba(0, 0, 0, 0.2)); /* Lighter shadow on click */
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