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Model: AdaReasoner/AdaReasoner-7B-Non-Randomized Source: Original Platform
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README.md
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README.md
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---
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license: apache-2.0
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datasets:
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- AdaReasoner/AdaReasoner-TC-Randomized
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- AdaReasoner/AdaReasoner-TG-Data
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language:
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- en
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metrics:
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- accuracy
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base_model:
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- Qwen/Qwen2.5-VL-7B-Instruct
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pipeline_tag: image-text-to-text
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tags:
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- agent
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---
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<div align="center">
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<img src="logo.png" alt="Logo" width="300">
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<h1 align="center">Dynamic Tool Orchestration for Iterative Visual Reasoning</h1>
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<a href="#">
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<img src="https://img.shields.io/badge/Paper-A42C25?style=for-the-badge&logo=arxiv&logoColor=white" alt="Paper">
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</a>
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<a href="https://github.com/ssmisya/AdaReasoner/tree/main/docs">
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<img src="https://img.shields.io/badge/Docs-1f6feb?style=for-the-badge&logo=readthedocs&logoColor=white" alt="Docs">
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</a>
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<a href="https://huggingface.co/collections/hitsmy/adareasoner">
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<img src="https://img.shields.io/badge/Data%20%26%20Model-fcd022?style=for-the-badge&logo=huggingface&logoColor=000" alt="Data & Model">
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</a>
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<a href="https://adareasoner.github.io">
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<img src="https://img.shields.io/badge/Homepage-2ea44f?style=for-the-badge&logo=googlechrome&logoColor=white" alt="Homepage">
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</a>
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<a href="https://github.com/ssmisya/AdaReasoner/tree/main/tool_server/tf_eval/demo">
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<img src="https://img.shields.io/badge/Demo-FF7C00?style=for-the-badge&logo=gradio&logoColor=white" alt="Demo">
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</a>
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<a href="https://www.youtube.com/watch?v=AtBoJYW_yDA">
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<img src="https://img.shields.io/badge/Video-FF0000?style=for-the-badge&logo=youtube&logoColor=white" alt="Video">
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</a>
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</div>
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---
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## 📋 Model Description
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**AdaReasoner-7B** is a vision-language model trained with dynamic tool orchestration capabilities for iterative visual reasoning. This model is AdaReasoner-7B-Non-Randomized.
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We provide three variants of AdaReasoner-7B, each optimized for different use cases:
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| Model | Description | Hugging Face |
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|------|-------------|--------------|
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| **AdaReasoner-7B-Randomized** | Trained with the *adaptive learning* method, enabling strong generalization to **unseen tools and tasks**. Designed for open-ended and evolving tool environments where adaptability is required. | [🤗 Link](https://huggingface.co/AdaReasoner/AdaReasoner-7B-Randomized) |
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| **AdaReasoner-7B-Non-Randomized** | Trained **without adaptive learning**, providing **more stable and reliable performance on known tools and tasks**, but limited generalization to unseen tools or task settings. | [🤗 Link](https://huggingface.co/AdaReasoner/AdaReasoner-7B-Non-Randomized) |
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| **AdaReasoner-VSP-7B** | Task-specialized model trained **exclusively on the Visual Spatial Planning (VSP) task**, achieving strong performance on VSP benchmarks but not intended for cross-task generalization. | [🤗 Link](https://huggingface.co/AdaReasoner/AdaReasoner-VSP-7B) |
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**Key Differences:**
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- **Randomized**: Trained with adaptive learning method, enabling zero-shot generalization to novel tools and task configurations
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- **Non-Randomized**: Trained without adaptive learning, offering more predictable behavior on familiar tools but lacking generalization
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- **VSP-7B**: Task-specific model fine-tuned exclusively on Visual Spatial Planning (VSP) benchmarks for optimal performance on navigation tasks
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## 🚀 Quick Start
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AdaReasoner-7B can be deployed for single-turn inference using standard inference frameworks such as vLLM.
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However, AdaReasoner is a tool-planning model whose full capabilities require interaction with an external tool environment.
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To fully evaluate or utilize its tool-planning behavior, we recommend using [AdaEval](https://github.com/ssmisya/AdaReasoner/tree/main/tool_server/tf_eval) provided in our repository for batch inference and evaluation, or trying the [Demo](https://github.com/ssmisya/AdaReasoner/tree/main/tool_server/tf_eval/demo) interface for interactive, single-instance GUI-based reasoning.
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## 🎯 Capabilities
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The model supports a diverse set of visual reasoning tasks, covering both structured reasoning and open-ended visual understanding:
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- **Visual Spatial Planning**
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Navigation and verification tasks based on grid-world environments (VSPO and VSP), evaluating fine-grained spatial perception, multi-step path planning, and safety verification under out-of-distribution map configurations.
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- **Compositional Visual Reasoning (Jigsaw)**
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Image reconstruction from shuffled patches (Jigsaw-COCO and BLINK-J), testing local–global consistency, part–whole reasoning, and visual compositional understanding.
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- **GUI Question Answering (GUIQA)**
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Fine-grained reasoning over GUI screenshots, including interactive webpage understanding (GUIChat) and agent-centric UI reasoning from WebMMU (Agentic Action subset), emphasizing element grounding, action planning, and multi-step inference.
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- **General Visual Question Answering (General VQA)**
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Open-ended visual reasoning beyond structured settings, evaluated on V* and HRBench, focusing on fine-grained visual search, attribute recognition, spatial relationship reasoning, and robustness to high-resolution, complex real-world scenes.
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## 🛠️ Tool Integration
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For full tool-augmented inference capabilities, please refer to the [AdaReasoner repository](https://github.com/ssmisya/AdaReasoner) which includes:
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- Tool Server deployment
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- AdaEval evaluation framework
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- Complete inference pipeline
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## 📊 Performance
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Please refer to our paper for detailed benchmark results across multiple visual reasoning tasks.
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## 🔧 Technical Details
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- **Base Architecture**: Qwen 2.5 VL 7B Instruct
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- **Training Method**: Tool Cold Start (SFT) + Tool GRPO (RL) + Adaptive Learning
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- **Context Length**: Support for extended context with multiple tool interactions
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- **Modalities**: Text + Vision
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## 📚 Citation
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If you use this model in your research, please cite:
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```bibtex
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@article{song2026adareasoner,
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title={AdaReasoner: Dynamic Tool Orchestration for Iterative Visual Reasoning},
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author={Song, Mingyang and Sun, Haoyu and Gu, Jiawei and Li, Linjie and Xu, Luxin and Krishna, Ranjay and Cheng, Yu},
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journal={arXiv preprint arXiv:2601.18631},
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year={2026}
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}
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```
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## 📄 License
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Apache 2.0
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## 🤝 Acknowledgments
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This model is part of the AdaReasoner project. For more information, visit our [GitHub repository](https://github.com/ssmisya/AdaReasoner).
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## 📧 Contact
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For questions and feedback, please open an issue in our [GitHub repository](https://github.com/ssmisya/AdaReasoner).
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