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README.md

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+---
+license: apache-2.0
+language:
+- en
+base_model: Qwen/Qwen2.5-VL-7B-Instruct
+tags:
+- vision
+- multimodal
+- visual-question-answering
+- memory-agent
+- long-context
+pipeline_tag: visual-question-answering
+---
+
+# MemOCR: Layout-Aware Visual Memory for Efficient Long-Horizon Reasoning
+
+<div align="center">
+
+<p align="center">
+  <a href="https://arxiv.org/abs/2601.21468"><img src="https://img.shields.io/badge/arXiv-2601.21468-b31b1b.svg" alt="arXiv"></a>
+  <a href="https://huggingface.co/papers/2601.21468"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging_Face-Paper-yellow" alt="HuggingFace Paper"></a>
+  <a href="https://meituan.github.io/MemOCR/"><img src="https://img.shields.io/badge/Project-Page-green" alt="Project Page"></a>
+  <a href="https://github.com/meituan/MemOCR"><img src="https://img.shields.io/badge/GitHub-Code-black?logo=github" alt="GitHub"></a>
+  <a href="./LICENSE"><img src="https://img.shields.io/badge/License-Apache%202.0-blue.svg" alt="License"></a>
+</p>
+</div>
+
+## 🌟 Model Description
+
+MemOCR is a visual memory agent that dynamically adapts information density during memory drafting and reading, and optimizes visual layouts to highlight key information.
+This checkpoint is fine-tuned from `Qwen2.5-VL-7B-Instruct` with budget-aware training objectives.
+
+### Key Capabilities
+- **Adaptive Information Density**: Dynamically adjusts memory content richness based on task requirements
+- **Budget-Aware Memory**: Optimizes memory usage with explicit token budget constraints
+- **Dual-Domain Architecture**: Separate memory drafting (text domain) and reading (vision domain) processes
+- **Multi-Hop Reasoning**: Superior performance on complex question answering tasks
+
+## 🏗️ Architecture
+
+<div align="center">
+<img src="https://raw.githubusercontent.com/meituan/MemOCR/main/docs/figs/memocr_method_architecture.png" width="95%" alt="MemOCR Architecture">
+</div>
+
+MemOCR consists of two main components:
+
+1. **Memory Drafting in Text Domain**: An LLM agent iteratively refines rich-text memory content based on question-answering feedback
+2. **Memory Reading in Vision Domain**: A vision-language model processes rendered visual memory with optimized information density
+
+The framework employs budget-aware training objectives to balance memory informativeness and token efficiency.
+
+<div align="center">
+<img src="https://raw.githubusercontent.com/meituan/MemOCR/main/docs/figs/memocr_method_augmentation.png" width="50%" alt="Data Augmentation Strategy">
+</div>
+
+## 📊 Performance
+
+### Main Results
+
+<div align="center">
+<img src="https://raw.githubusercontent.com/meituan/MemOCR/main/docs/figs/memocr_main_results.png" width="90%" alt="Main Results">
+</div>
+
+MemOCR achieves state-of-the-art performance across multiple multi-hop QA benchmarks:
+- **HotpotQA**: Superior accuracy with efficient memory budgets
+- **2WikiMultihopQA**: Strong multi-hop reasoning capabilities
+- **NaturalQuestions & TriviaQA**: Excellent knowledge retrieval performance
+
+### Ablation Studies
+
+<div align="center">
+<img src="https://raw.githubusercontent.com/meituan/MemOCR/main/docs/figs/memocr_ablation.png" width="75%" alt="Ablation Studies">
+</div>
+
+### Analysis: Information Density & Budget
+
+<div align="center">
+<img src="https://raw.githubusercontent.com/meituan/MemOCR/main/docs/figs/memocr_analysis_budget.png" width="45%" style="display:inline-block">
+<img src="https://raw.githubusercontent.com/meituan/MemOCR/main/docs/figs/memocr_analysis_density.png" width="45%" style="display:inline-block">
+</div>
+
+## 🚀 Usage
+
+This model is designed to work with the MemOCR framework. Please refer to the [official repository](https://github.com/meituan/MemOCR) for detailed usage instructions.
+
+### Quick Start
+
+```python
+# Install dependencies
+pip install transformers torch
+
+# Load model (example)
+from transformers import AutoModel, AutoProcessor
+
+model = AutoModel.from_pretrained("your-username/MemOCR-step900")
+processor = AutoProcessor.from_pretrained("your-username/MemOCR-step900")
+```
+
+For complete training and evaluation pipelines, please visit the [GitHub repository](https://github.com/meituan/MemOCR).
+
+## 📚 Citation
+
+If you find MemOCR useful in your research, please consider citing:
+
+```bibtex
+@article{shi2026memocr,
+  title={MemOCR: Layout-Aware Visual Memory for Efficient Long-Horizon Reasoning},
+  author={Yaorui Shi and Shugui Liu and Yu Yang and Wenyu Mao and Yuxin Chen and Qi GU and Hui Su and Xunliang Cai and Xiang Wang and An Zhang},
+  journal={arXiv preprint arXiv:2601.21468},
+  year={2026},
+}
+```
+
+## 🙏 Acknowledgements
+
+This model is built upon:
+- **[Qwen2.5-VL](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct)** as the vision-language model backbone
+- **[veRL](https://github.com/volcengine/verl)** as the reinforcement learning training framework
+- **[MemAgent](https://github.com/BytedTsinghua-SIA/MemAgent)** for the recurrent module and training dataset
+
+## 📄 License
+
+This model is licensed under the Apache License 2.0. See the LICENSE file for details.
+
+### Dataset License
+
+Training and evaluation datasets are derived from:
+- **HotpotQA, 2WikiMultihopQA, Natural Questions, TriviaQA**: Wikipedia-derived content licensed under [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/)
+