Add pipeline tag and link to paper

README.md

@@ -1,16 +1,38 @@
 ---
-datasets:
-- earth-insights/EarthReason
 base_model:
 - Qwen/Qwen2.5-VL-7B-Instruct
+datasets:
+- earth-insights/EarthReason
 library_name: transformers
+pipeline_tag: image-segmentation
+license: apache-2.0
 ---
 
+# Bridging Semantics and Geometry: A Decoupled LVLM–SAM Framework for Reasoning Segmentation in Remote Sensing
+
+This repository contains the 7B model of **Think2Seg-RS**, a decoupled framework for reasoning segmentation in remote sensing (RS) imagery. 
+
+The model was introduced in the paper [Bridging Semantics and Geometry: A Decoupled LVLM-SAM Framework for Reasoning Segmentation in Remote Sensing](https://huggingface.co/papers/2512.19302).
+
+## Overview
+
+Think2Seg-RS decouples high-level semantic reasoning from low-level geometric execution. It trains an LVLM prompter (based on Qwen-2.5-VL) to control a frozen Segment Anything Model (SAM2) via structured geometric prompts. Through a mask-only reinforcement learning objective, the LVLM learns to translate abstract semantic reasoning into spatially grounded actions, achieving state-of-the-art performance on the EarthReason dataset.
+
+## Resources
 
-## Bridging Semantics and Geometry: A Decoupled LVLM–SAM Framework for Reasoning Segmentation in Remote Sensing
+- **Paper:** [arXiv:2512.19302](https://huggingface.co/papers/2512.19302)
+- **Code:** [GitHub - Think2Seg-RS](https://github.com/Ricardo-XZ/Think2Seg-RS)
+- **Dataset:** [EarthReason](https://huggingface.co/datasets/earth-insights/EarthReason)
 
-This is the 7B model of [Think2Seg-RS](https://github.com/Ricardo-XZ/Think2Seg-RS), a decoupled framework for reasoning segmentation in remote sensing (RS) imagery.
+## Citation
 
-Our core idea is to decouple high-level semantic reasoning from low-level geometric execution. Specifically, we train an LVLM prompter (e.g., Qwen-2.5-VL) to control a frozen Segment Anything Model (SAM2) via structured geometric prompts. Through a result-oriented reinforcement learning objective, the LVLM learns to translate abstract semantic reasoning into spatially grounded actions, achieving state-of-the-art performance on the EarthReason dataset.
+If you find this work helpful for your research, please cite:
 
-For more details, code, and the complete framework, please visit our [GitHub repository](https://github.com/Ricardo-XZ/Think2Seg-RS).
+```bibtex
+@article{think2seg_rs_2025,
+  title={Bridging Semantics and Geometry: A Decoupled LVLM-SAM Framework for Reasoning Segmentation in Remote Sensing},
+  author={Anonymous},
+  journal={arXiv preprint arXiv:2512.19302},
+  year={2025}
+}
+```