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@@ -123,23 +123,22 @@ <h1 class="title is-1 publication-title">A Multimodal Automated Interpretability
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         <h2 class="title is-3 has-text-centered">MAIA</h2>
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-            <img src="./static/figures/MAIA_schematic.png" alt="MAIA Schematic" style="margin-right: 20px; width: 40%;">
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-              We describe MAIA, a Multimodal Automated Interpretability Agent. MAIA is a system that uses neural models to automate neural model understanding tasks like feature interpretation and failure mode discovery.
-              It equips a pre-trained vision-language model with a set of tools that support iterative experimentation on subcomponents of other models to explain their behavior. These include tools commonly used by human interpretability researchers: for synthesizing and editing inputs, computing maximally activating exemplars from real-world datasets, and summarizing and describing experimental results.
-              <i>Interpretability experiments</i> proposed by MAIA compose these tools to describe and explain system behavior.
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+        <div style="display: flex; align-items: center;">
+          <img src="./static/figures/MAIA_schematic.png" alt="MAIA Schematic" style="margin-right: 20px; width: 40%;">
+          <p style="text-align: justify;">
+            We describe MAIA, a Multimodal Automated Interpretability Agent. MAIA is a system that uses neural models to automate neural model understanding tasks like feature interpretation and failure mode discovery.
+            It equips a pre-trained vision-language model with a set of tools that support iterative experimentation on subcomponents of other models to explain their behavior. These include tools commonly used by human interpretability researchers: for synthesizing and editing inputs, computing maximally activating exemplars from real-world datasets, and summarizing and describing experimental results.
+            <i>Interpretability experiments</i> proposed by MAIA compose these tools to describe and explain system behavior.
+          </p>
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+        <hr>
+      </div>
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