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README.md

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-license: cc-by-4.0
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+---
+tags:
+- Visual Question-Visual Answering (VQVA)
+- dataset-construction
+- image-editing
+- multimodal
+- instruction-tuning
+- visual-reasoning
+---
+
+# 🥯 **BAGEL-World-model**
+
+**A agentic data-centric framework for producing large-scale interleaved Visual Question–Visual Answering (VQ-VA) data.**
+
+
+---
+
+The BAGEL-World framework outputs high-quality VQ-VA data via the following steps:
+
+### 🔄**Preprocessing**
+
+Filters and classify noisy web-interleaved data into design- and knowledge-related documents.
+
+
+### 🤖**Agentic Pipeline for VQ-VA Data Creation**
+
+**1. Retriever** selects image pairs containing non-trivial transformations from interleaved documents that can serve as the basis for free-form questions.  
+
+**2. Instruction** Generator write a natural-language question about one image so that the other image serves as the correct answer.  
+
+**3. Filterer** removes low-quality triplets ⟨Question Image, Question Text, Answer Image⟩.  
+
+**4. Rewriter** increases instruction diversity by producing multiple variants of the original questions.  
+
+**5. Reasoner** generates a language-based chain-of-thought explanation describing how the source image should be transformed to obtain the target image.  
+
+The framework at last outputs **interleaved quadruplets**:
+
+- 🧠 *Question Image*  
+- 💬 *Visual Question / Instruction*  
+- 🔍 *Reasoning Trace*  
+- 🎨 *Answer Image*
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+
+
+
+
+Stay tuned for updates and examples!