Create README.md

README.md

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+---
+language:
+- en
+tags:
+- vision-language
+- vqa
+- diagnostic-benchmark
+- xai
+license: mit
+task_categories:
+- visual-question-answering
+---
+
+# GridVQA-X Datasets
+
+GridVQA-X is the first diagnostic framework designed to objectively evaluate the faithfulness of post-hoc cross-modal explainers. By utilizing a closed-world synthesis logic with mathematically guaranteed unique ground-truth explanations, it provides a controlled testbed to isolate genuine cross-modal spatial reasoning from shallow shortcuts.
+
+## Dataset Summary
+The dataset features **S × S** visual grids populated by geometric objects, paired with multi-hop spatial reasoning queries. Each query defines a **Target** object to find/count and one or more **Anchor** reference objects connected by directional tokens.
+
+The dataset contains two distinct, parallel splits:
+*   **D_pure (The Causal Split):** Systematically removes all statistical heuristics. It algorithmically populates invalid spatial regions with adversarial distractors matching target attributes, forcing models to utilize complete multi-hop spatial geometry.
+*   **D_spur (The Shortcut Split):** Intentionally preserves the pervasive "Bag-of-Words Alignment" cross-modal shortcut. No target-attribute objects are placed outside the valid spatial region, creating a behavioral trap where the probability of the answer given target attributes is 1.0.
+
+## Dataset Taxonomy and Configurations
+Every sample is parameterized by a strict 4-tuple configuration: **(Depth, QType, Form, Density)**
+
+*   **Spatial Depth (1, 2, or 3):** Controls the number of anchor objects and relational multi-hop complexity.
+*   **Question Type (QType):** 
+    *   `Attribute-Only (A)`: Non-spatial baseline queries.
+    *   `Shape-Only (SO)` / `Color-Only (CO)`: Isolates directional reasoning to a single feature type.
+    *   `Mixed (M)`: Requires multimodal binding of shape and color for both target and anchor.
+    *   `Comparison (CMP)`: Multi-step logical operations comparing object counts.
+*   **Form (0 or 1):** Alters the target task header between counting (`0`) and existence (`1`) to prevent statistical format overfitting.
+*   **Density (d_0.3 or d_0.7):** Adjusts grid object count to test spatial localization fidelity against background noise.
+
+## Ground-Truth Explanations
+The dataset provides a mathematically proven, unique ground-truth causal explanation. The ground-truth visual masks are strictly bounded to the target and anchor items (**A ∪ T**), leaving all other distractor objects with a causal effect of exactly zero.