Update index.html

Author: MJXWANG

index.html

@@ -498,23 +498,23 @@ <h2 class="title is-3 has-text-centered">🧭 Implications & Future Work</h2>
 
       <h3 class="title is-5">🧠 Implications for Cognitive AI</h3>
       <ul>
-        <li><strong>Emergent Congruency:</strong> The classic Stroop and Flanker effects emerge even in untrained models, suggesting interference control arises from foundational pretraining dynamics.</li>
-        <li><strong>Limits of Scaling:</strong> Even 110B-parameter models fail on deeply nested conflicts, pointing to architectural or training regime bottlenecks beyond sheer size.</li>
-        <li><strong>Shared Control Mechanisms:</strong> Strong correlations between letter- and number-Flanker scores (r = 0.96) support the idea of a unified control construct within VLMs.</li>
+        <li><strong>Emergent Congruency:</strong> Stroop and Flanker effects emerge reliably across model scales, with <em>graded variation</em> in sensitivity — indicating that interference resolution arises from general-purpose associative dynamics.</li>
+        <li><strong>Hierarchical Bottlenecks:</strong> Squared tasks expose <em>residual conflict sensitivity</em> even in frontier models, suggesting that scaling alone cannot resolve higher-order control limitations.</li>
+        <li><strong>Shared Control Mechanisms:</strong> Strong correlations between letter- and number-Flanker scores (r = 0.96) support the existence of a unified control construct within VLMs, suggesting <em>stable, trait-like signatures</em> of cognitive control.</li>
       </ul>
 
       <h3 class="title is-5">🔍 Open Questions</h3>
       <ul>
-        <li>How do specific aspects of pretraining data influence the emergence of conflict control?</li>
-        <li>What inductive biases or training interventions are needed to overcome hierarchical interference?</li>
-        <li>Can VLMs develop temporally extended control structures analogous to executive functions?</li>
+        <li>What aspects of pretraining distribution influence <em>trait emergence</em> in control tasks?</li>
+        <li>What architectural or procedural inductive biases support <em>deep hierarchical interference resolution</em>?</li>
+        <li>Can VLMs develop <em>temporally persistent</em> control structures akin to executive functions in biological agents?</li>
       </ul>
 
       <h3 class="title is-5">🧪 Methodological Contribution</h3>
       <p>
-        This study introduces a psychophysics-inspired paradigm for evaluating control behavior in VLMs. 
-        By using tightly controlled stimuli and separating standard vs. hierarchical conflict, we provide 
-        mechanistic insights and open a new path toward studying cognitive control in AI systems.
+        This study proposes a <strong>psychophysics-inspired trait measurement framework</strong> for evaluating control behaviors in VLMs.
+        By using <em>minimally confounded trials</em> and contrastive conflict structures, we isolate underlying control properties and enable
+        <em>structural comparisons</em> across models.
       </p>
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