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@@ -290,7 +290,8 @@ <h3 class="title is-4 has-text-centered">Preliminary</h3>
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<!-- VisRAG -->
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<div class="column is-half">
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<p class="content has-text-justified">
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<b>Vision-based RAG (VisRAG):</b> Given a textual query \(q\) (e.g., a question or instruction) and a visual corpus \( \mathcal{V} = \{ X_i \}_{i=1}^{N} \),
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<b>Vision-based RAG (VisRAG):</b>
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Given a textual query \(q\) (e.g., a question or instruction) and a visual corpus \( \mathcal{V} = \{ X_i \}_{i=1}^{N} \),
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VisRAG retrieves the top-\(k\) most relevant document images and generates a response as follows:
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\[ \underbrace{R}_{\text{top-}k\ \text{retrieved doc images}}
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= \mathcal{R}\!\big(q,\; \mathcal{E}_r(\mathcal{V})\big)
@@ -307,12 +308,27 @@ <h3 class="title is-4 has-text-centered">Preliminary</h3>
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<!-- SCM -->
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<div class="column is-half">
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<p class="content has-text-justified">
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<b>Structural Causal Model (SCM):</b> We model semantic content S and degradation D as independent causes of the observed image X:
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ABC
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<b>Structural Causal Model (SCM):</b>
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We model semantic content \( S \) and degradation \( D \) as independent causes of the observed image \( X \).
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In standard encoders, both factors are entangled after conditioning on \( X \), leading to degradation leakage in downstream predictions \( A \).
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The overall causal structure can be summarized as:
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\[
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S \rightarrow X \leftarrow D, \qquad
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X \rightarrow Z \rightarrow A
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\]
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Our goal is to block the <span class="tag is-light" style="background-color: #ffeef0; color: #b30033;">non-causal path</span>:
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\[
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D \rightarrow X \rightarrow Z \rightarrow A,
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\]
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while preserving the <span class="tag is-light" style="background-color: #ffeef0; color: #1e5e1e;">causal path</span>:
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\[
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S \rightarrow X \rightarrow Z \rightarrow A
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\]
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</p>
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</div>
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</div>
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<h3 class="title is-4 has-text-centered">RobustVisRAG</h3>
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<div class="columns is-multiline is-variable is-6">
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<!-- x -->

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