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+    /* Center all section titles like the hero title */
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-<!-- Results and Analysis -->
+<!-- Results and Analysis (+ Discussion & Conclusion appended at the end) -->
 <section class="section" id="results-analysis">
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             token lengths. Right: task-wise performance across hotspot categories.
           </figcaption>
         </figure>
+
+        <!-- Discussion -->
+        <div class="content has-text-justified" style="margin-top:28px;">
+          <h3 class="title is-4 has-text-centered">Discussion</h3>
+          <p>
+            GPS signals are indispensable for providing geographic context in automotive agents, yet they
+            are prone to disruptions in real-world environments such as tunnels, underground parking, or dense
+            urban canyons. These interruptions can cause temporary localization failures, directly undermining
+            navigation and geo-dependent decision-making. To address this limitation, large language models
+            (LLMs) can act as virtual sensors by leveraging their built-in knowledge of road networks together
+            with the last available GPS coordinates and timestamps. During short signal outages, the agent can
+            simulate intermediate positions and continue offering navigation or context-aware recommendations.
+            Once connectivity is restored, the simulated trajectory can be aligned with actual positioning
+            data. This capability highlights the potential of LLMs to complement imperfect sensor signals and
+            enhance robustness in safety-critical automotive applications.
+          </p>
+        </div>
+
+        <!-- Conclusion -->
+        <div class="content has-text-justified" style="margin-top:18px;">
+          <h3 class="title is-4 has-text-centered">Conclusion</h3>
+          <p>
+            In this work, we present <strong>Automotive-ENV</strong>, the first large-scale benchmark explicitly designed for
+            evaluating multimodal agents in realistic automotive GUI environments. Unlike desktop or mobile
+            benchmarks, Automotive-ENV provides structured, reproducible, and geographically parameterized
+            tasks that capture the complexity of in-vehicle interaction under real-world constraints. Building on
+            this foundation, we propose <strong>ASURADA</strong>, a geo-adaptive agent capable of integrating GPS location
+            and contextual signals to deliver safe and personalized actions. Our experiments show that geo-context
+            integration not only improves task accuracy, especially in safety-critical settings, but also
+            reduces reasoning overhead by enabling proactive, context-driven planning. Together, Automotive-ENV
+            and ASURADA establish a foundation for the next generation of in-vehicle assistants that are
+            multimodal, safety-aware, and culturally adaptive, advancing the reliable deployment of autonomous
+            agents in high-stakes driving environments.
+          </p>
+        </div>
+
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