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Model: AdaReasoner/AdaReasoner-7B-Non-Randomized
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---
license: apache-2.0
datasets:
- AdaReasoner/AdaReasoner-TC-Randomized
- AdaReasoner/AdaReasoner-TG-Data
language:
- en
metrics:
- accuracy
base_model:
- Qwen/Qwen2.5-VL-7B-Instruct
pipeline_tag: image-text-to-text
tags:
- agent
---
<div align="center">
<img src="logo.png" alt="Logo" width="300">
<h1 align="center">Dynamic Tool Orchestration for Iterative Visual Reasoning</h1>
<a href="#">
<img src="https://img.shields.io/badge/Paper-A42C25?style=for-the-badge&logo=arxiv&logoColor=white" alt="Paper">
</a>
<a href="https://github.com/ssmisya/AdaReasoner/tree/main/docs">
<img src="https://img.shields.io/badge/Docs-1f6feb?style=for-the-badge&logo=readthedocs&logoColor=white" alt="Docs">
</a>
<a href="https://huggingface.co/collections/hitsmy/adareasoner">
<img src="https://img.shields.io/badge/Data%20%26%20Model-fcd022?style=for-the-badge&logo=huggingface&logoColor=000" alt="Data & Model">
</a>
<a href="https://adareasoner.github.io">
<img src="https://img.shields.io/badge/Homepage-2ea44f?style=for-the-badge&logo=googlechrome&logoColor=white" alt="Homepage">
</a>
<a href="https://github.com/ssmisya/AdaReasoner/tree/main/tool_server/tf_eval/demo">
<img src="https://img.shields.io/badge/Demo-FF7C00?style=for-the-badge&logo=gradio&logoColor=white" alt="Demo">
</a>
<a href="https://www.youtube.com/watch?v=AtBoJYW_yDA">
<img src="https://img.shields.io/badge/Video-FF0000?style=for-the-badge&logo=youtube&logoColor=white" alt="Video">
</a>
</div>
---
## 📋 Model Description
**AdaReasoner-7B** is a vision-language model trained with dynamic tool orchestration capabilities for iterative visual reasoning. This model is AdaReasoner-7B-Non-Randomized.
We provide three variants of AdaReasoner-7B, each optimized for different use cases:
| Model | Description | Hugging Face |
|------|-------------|--------------|
| **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) |
| **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) |
| **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) |
**Key Differences:**
- **Randomized**: Trained with adaptive learning method, enabling zero-shot generalization to novel tools and task configurations
- **Non-Randomized**: Trained without adaptive learning, offering more predictable behavior on familiar tools but lacking generalization
- **VSP-7B**: Task-specific model fine-tuned exclusively on Visual Spatial Planning (VSP) benchmarks for optimal performance on navigation tasks
## 🚀 Quick Start
AdaReasoner-7B can be deployed for single-turn inference using standard inference frameworks such as vLLM.
However, AdaReasoner is a tool-planning model whose full capabilities require interaction with an external tool environment.
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.
## 🎯 Capabilities
The model supports a diverse set of visual reasoning tasks, covering both structured reasoning and open-ended visual understanding:
- **Visual Spatial Planning**
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.
- **Compositional Visual Reasoning (Jigsaw)**
Image reconstruction from shuffled patches (Jigsaw-COCO and BLINK-J), testing local–global consistency, part–whole reasoning, and visual compositional understanding.
- **GUI Question Answering (GUIQA)**
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.
- **General Visual Question Answering (General VQA)**
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.
## 🛠️ Tool Integration
For full tool-augmented inference capabilities, please refer to the [AdaReasoner repository](https://github.com/ssmisya/AdaReasoner) which includes:
- Tool Server deployment
- AdaEval evaluation framework
- Complete inference pipeline
## 📊 Performance
Please refer to our paper for detailed benchmark results across multiple visual reasoning tasks.
## 🔧 Technical Details
- **Base Architecture**: Qwen 2.5 VL 7B Instruct
- **Training Method**: Tool Cold Start (SFT) + Tool GRPO (RL) + Adaptive Learning
- **Context Length**: Support for extended context with multiple tool interactions
- **Modalities**: Text + Vision
## 📚 Citation
If you use this model in your research, please cite:
```bibtex
@article{song2026adareasoner,
title={AdaReasoner: Dynamic Tool Orchestration for Iterative Visual Reasoning},
author={Song, Mingyang and Sun, Haoyu and Gu, Jiawei and Li, Linjie and Xu, Luxin and Krishna, Ranjay and Cheng, Yu},
journal={arXiv preprint arXiv:2601.18631},
year={2026}
}
```
## 📄 License
Apache 2.0
## 🤝 Acknowledgments
This model is part of the AdaReasoner project. For more information, visit our [GitHub repository](https://github.com/ssmisya/AdaReasoner).
## 📧 Contact
For questions and feedback, please open an issue in our [GitHub repository](https://github.com/ssmisya/AdaReasoner).

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"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'You are a helpful assistant.' }}\n {%- endif %}\n {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0]['role'] == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n {%- else %}\n {{- '<|im_start|>system\\nYou are a helpful assistant.<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|im_start|>' + message.role }}\n {%- if message.content %}\n {{- '\\n' + message.content }}\n {%- endif %}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- message.content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n",
"clean_up_tokenization_spaces": false,
"eos_token": "<|im_end|>",
"errors": "replace",
"extra_special_tokens": {},
"model_max_length": 131072,
"pad_token": "<|endoftext|>",
"padding_side": "right",
"processor_class": "Qwen2_5_VLProcessor",
"split_special_tokens": false,
"tokenizer_class": "Qwen2Tokenizer",
"unk_token": null
}

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vocab.json Normal file
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