MemOCR: Layout-Aware Visual Memory for Efficient Long-Horizon Reasoning
🌟 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
MemOCR consists of two main components:
- Memory Drafting in Text Domain: An LLM agent iteratively refines rich-text memory content based on question-answering feedback
- 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.
📊 Performance
Main Results
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
Analysis: Information Density & Budget
🚀 Usage
This model is designed to work with the MemOCR framework. Please refer to the official repository for detailed usage instructions.
📚 Citation
If you find MemOCR useful in your research, please consider citing:
@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 as the vision-language model backbone
- veRL as the reinforcement learning training framework
- 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