Create README.md

README.md

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+---
+language:
+- en
+tags:
+- vision-language
+- mdetr
+- xai
+license: mit
+model_index:
+- name: mdetr-gridvqa-pure
+  task: visual-question-answering
+- name: mdetr-gridvqa-spurious
+  task: visual-question-answering
+---
+
+# GridVQA-X Models
+
+This repository contains two paired reference models, **M_pure** and **M_spur**, built on identical transformer architectures (**MDETR**). These models, coupled with their corresponding datasets, together form a diagnostic framework to evaluate if Multimodal Explainable AI (MxAI) methods genuinely capture cross-modal synergy or simply report shallow feature correlations.
+
+## Model Descriptions
+
+### 1. M_pure (The Faithful Spatial Reasoner)
+*   **Training Distribution:** Trained exclusively on the D_pure dataset.
+*   **Behavioral Dynamics:** Trained via explanation-guided dynamics using a two-phase optimization. Phase 1 forces explicit visual-textual token alignment via L1 and generalized IoU losses. Phase 2 handles Question Answering using class-frequency weighted cross-entropy to completely eliminate answer prior biases. 
+*   **Capabilities:** Successfully internalizes true causal spatial-relational synergy, achieving robust accuracy across both clean and heavily distractor-crowded grids.
+
+### 2. M_spur (The Shortcut / Bag-of-Words Model)
+*   **Training Distribution:** Trained exclusively on the D_spur dataset.
+*   **Behavioral Dynamics:** Structurally forced to rely on cross-modal shortcuts during training. It skips relational spatial geometry entirely and maps keywords directly to target visual volume.
+*   **Capabilities:** Achieves perfect accuracy (1.000) on its native spurious distribution, but fails catastrophically when evaluated on D_pure multi-hop queries.
+
+## Intended Diagnostic Use
+These models are released explicitly to stress-test vision-language explainability algorithms (e.g., DIME, MultiSHAP, MultiViz, EMAP, InterSHAP):
+*   **The Litmus Test:** A faithful explainer *must* output completely different attribution heatmaps or synergy scalars for M_pure and M_spur on the same input question.
+*   **The Reality Check:** If your explainer highlights identical spatial regions for both models, it suffers from "model blindness" or is simply behaving as a superficial object detector.
+
+## Performance Benchmark Metrics
+
+| Evaluation Metric | M_pure on D_pure | M_spur on D_spur | M_spur on D_pure |
+| :--- | :---: | :---: | :---: |
+| **Global Accuracy** | >99% | 100% | **Catastrophic Failure** (8%-14% on multi-hop) |
+| **Causal Pathway** | True Spatial Relations | Bag-of-Words Shortcut | Unimodal Feature Collapse |