license: apache-2.0
task_categories:
- image-text-to-text
tags:
- visual-reasoning
- reinforcement-learning
- multi-source
- rlvr
MARS: Mono-Anchored Multi-Source Visual Reasoning
This repository contains the dataset for the paper "Does Seeing More Mean Knowing More? Mono-Anchored Advantage Normalization for Multi-Source Visual Reasoning".
- GitHub Repository: https://github.com/AI9Stars/MARS
- Paper: https://arxiv.org/abs/2605.25437
Introduction
MARS is a novel mono-anchored multi-source reasoning framework designed for Reinforcement Learning with Verifiable Rewards (RLVR). It treats each visual modality as an independent information source and uses mono-source rewards as dynamic anchors to explicitly incorporate information gain from multi-source fusion while suppressing potential noise or conflicts.
Dataset Summary
The dataset provides reinforcement fine-tuning (RL) data for several multi-source visual reasoning domains:
| Domain | Task Type |
|---|---|
| Infrared | Grounding tasks (RGB & IR) |
| Depth | Spatial reasoning using depth information |
| Multi-view | Reasoning across multiple perspectives |
| Text-rich | Visual reasoning in document-intensive environments |
For instructions on how to download the original images and prepare the data for training using the MARS framework, please refer to the official GitHub repository.
Citation
If you find this work useful, please cite our paper:
@article{zeng2026does,
title={Does Seeing More Mean Knowing More? Mono-Anchored Advantage Normalization for Multi-Source Visual Reasoning},
author={Zeng, Fanhu and Luo, Zhicong and Wang, Zefan and Li, You and Chen, Chi and Sun, Maosong},
journal={arXiv preprint arXiv:2605.25437},
year={2026}
}