GEOREASON: ALIGNING THINKING AND ANSWERING IN REMOTE SENSING VISION-LANGUAGE MODELS VIA LOGICAL CONSISTENCY REINFORCEMENT LEARNING

Wenshuai Li  Xiantai Xiang  Zixiao Wen  Guangyao Zhou  Ben Niu  Feng Wang  Lijia Huang  Qiantong Wang  Yuxin Hu 

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GeoReason: Overview

GeoReason is a framework designed for Remote Sensing Vision-Language Models (RS-VLMs) to address "logical hallucinations" and "pseudo-reasoning," where models derive correct answers from flawed logic or shortcuts. By introducing a logic-driven dataset (GeoReason-Bench) and employing a consistency-aware reinforcement learning strategy with a novel "Logical Consistency Reward," it compels the model to strictly anchor its final decisions in verifiable reasoning traces, ensuring both accuracy and cognitive reliability.

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Performance

Eevaluating models across Perceptual Tasks (Count, Color, Shape, Scene) and Reasoning Tasks (Reason) to analyze their multi-level understanding.

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Get Started

Environment Installation

conda create -n GeoReason python=3.10
conda activate GeoReason
pip install -r requirements.txt

Infer with GeoReason

You can use GeoReason_infer.py to generate the answers to the questions.

python GeoReason_infer.py --model_path /path/to/model --dataset /path/to/dataset --image_path /path/to/image_path

Citation

@misc{li2026georeasonaligningthinkinganswering, title={GeoReason: Aligning Thinking And Answering In Remote Sensing Vision-Language Models Via Logical Consistency Reinforcement Learning}, author={Wenshuai Li and Xiantai Xiang and Zixiao Wen and Guangyao Zhou and Ben Niu and Feng Wang and Lijia Huang and Qiantong Wang and Yuxin Hu}, year={2026}, eprint={2601.04118}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2601.04118}, }