---
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.