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README.md

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+---
+license: mit
+library_name: transformers
+pipeline_tag: image-text-to-text
+base_model: Qwen/Qwen2.5-VL-7B-Instruct
+---
+
+# Planning with the Views via Scene Self-Exploration
+
+This repository contains a model checkpoint presented in the paper [Planning with the Views via Scene Self-Exploration](https://huggingface.co/papers/2605.29563).
+
+[**Project Page**](https://viewsuite.github.io) | [**GitHub**](https://github.com/mll-lab-nu/ViewSuite) | [**Paper**](https://viewsuite.github.io/viewsuite_paper.pdf)
+
+## Overview
+
+Can VLMs predict how each camera move changes the view, and plan many such moves ahead? This capability, called **view planning**, requires (1) understanding how a single action transforms the view, and (2) composing many such transformations across multi-turn plans to identify a target view. 
+
+**ViewSuite** is a 3D point-cloud environment and benchmark suite for view planning, built on real ScanNet indoor scenes. It probes view planning through three diagnostic tasks:
+- **Path-to-View (P2V)**: Predict the resulting view from an action sequence.
+- **View-to-Path (V2P)**: Infer the action sequence between two views.
+- **Interactive View Planning (IVP)**: Plan view changes over multiple turns to identify a target view.
+
+This model is an optimized version of **Qwen2.5-VL-7B**, trained using an iterative framework that alternates self-exploration with view graph distillation. This approach significantly closes the planning gap found in frontier VLMs, improving performance on interactive view planning tasks.
+
+## Citation
+
+If you find ViewSuite or these checkpoints useful in your research, please consider citing:
+
+```bibtex
+@article{wang2026viewsuite,
+  title   = {Planning with the Views},
+  author  = {Wang, Kangrui and Li, Linjie and Yang, Zhengyuan and Chen, Shiqi and
+             Wang, Zihan and Fei-Fei, Li and Wu, Jiajun and Guibas, Leonidas and
+             Wang, Lijuan and Li, Manling},
+  year    = {2026}
+}
+```