SII-Monument-Valley/CiQi-VQA

CiQi-Agent

Github | Model | Dataset | Paper

CiQi-Agent: Aligning Vision, Tools and Aesthetics in Multimodal Agent for Cultural Reasoning on Chinese Porcelains

Accepted to ECCV 2026

🎯 Overview

CiQi-Agent has been accepted to ECCV 2026.

We present CiQi-Agent, a domain-specific multimodal agent for antique Chinese porcelain connoisseurship. The project is designed to combine fine-grained visual perception, tool-augmented reasoning, and cultural-heritage knowledge grounding for explainable porcelain analysis.

CiQi-Agent is built for tool-augmented multimodal reasoning on antique Chinese porcelains. During inference, it can inspect local visual evidence with an image zoom-in tool, retrieve visually similar examples with image retrieval, and access relevant domain knowledge with text retrieval, enabling more grounded and interpretable connoisseurship analysis.

Alongside the model, we release a single Hugging Face dataset repository that contains both the training data and benchmark data:

• —CiQi-VQA, covering 29,596 porcelain specimens, 51,553 images, and 557,943 VQA pairs
• —CiQi-Bench, the benchmark portion included in the CiQi-VQA repository, built from 775 porcelain specimens, 878 images, and 5,425 multiple-choice questions

On CiQi-Bench, CiQi-Agent achieves 81.5% average accuracy on multiple-choice evaluation and 66.7% average score on free-form evaluation, outperforming strong open-source and closed-source multimodal baselines.

🤖 Model

• —Released as [SII-Monument-Valley/CiQi-Agent-7B](https://huggingface.co/SII-Monument-Valley/CiQi-Agent-7B)
• —Built on [Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct) with 8.29B parameters
• —Uses tool-augmented reasoning with zoom-in, image retrieval, and text retrieval
• —Trained with a two-phase supervised fine-tuning + reinforcement learning pipeline
• —Evaluated on CiQi-Bench with both multiple-choice and free-form protocols
• —Achieves 81.5% average accuracy on multiple-choice evaluation, exceeding GPT-5 by 5.7 points and the strongest listed open-source baseline GLM-4.5V (72.6%) by 8.9 points
• —Achieves 66.7% average score on free-form evaluation, exceeding GPT-5 by 18.7 points and Qwen2.5-VL-72B-Instruct by 23.7 points

The released checkpoint is stored in FP32 (approximately 33.2 GB) and can be downcast to BF16 by SGLang while loading. A conservative 4090 profile with a 4K context, an 8192-token KV cache pool, one running request, and CUDA graph disabled completed an OpenAI-compatible chat request using 17,676 MiB. The measurement was collected on a platform-provided 48 GB RTX 4090; it is below a 24 GB memory budget, but a standard 24 GB RTX 4090 was not directly tested. H200-class hardware remains recommended for longer contexts, higher concurrency, and production deployment. See the model card for the validated SGLang commands and limitations.

📊 Model Performance

Multiple-Choice Accuracy (%) on CiQi-Bench

Free-Form Score (%) on CiQi-Bench

📦 Dataset & Benchmark

The [SII-Monument-Valley/CiQi-VQA](https://huggingface.co/datasets/SII-Monument-Valley/CiQi-VQA) repository contains both the CiQi-VQA training data and the CiQi-Bench evaluation data.

📊 CiQi-VQA Training Data

CiQi-VQA is a large-scale dataset for porcelain-centered multimodal training.

• —29,596 porcelain specimens
• —51,553 images
• —557,943 VQA pairs
• —38 dynasties
• —42 reign periods
• —246 glaze color categories
• —248 decorative motif categories
• —158 vessel shape categories

Link: https://huggingface.co/datasets/SII-Monument-Valley/CiQi-VQA

🧪 CiQi-Bench Evaluation Data

CiQi-Bench is the expert-aligned benchmark portion of the same CiQi-VQA repository for evaluating porcelain connoisseurship ability.

• —775 porcelain specimens
• —878 images
• —5,425 multiple-choice questions
• —Free-form evaluation with attribute-wise scoring

Repository: https://huggingface.co/datasets/SII-Monument-Valley/CiQi-VQA

📈 Dataset and Benchmark Statistics

📜 License

• —Model license: CC BY-NC 4.0
• —Dataset license: CC BY-NC 4.0

Users are also responsible for complying with the licenses and terms of the base model, source images, and any external retrieval corpus they use.

🤝 Acknowledgement

We thank [Verl](https://github.com/volcengine/verl) for providing an open-source reinforcement learning framework that supports this line of research.

We also thank [DeepEyes](https://github.com/Visual-Agent/DeepEyes) for inspiring and informing our exploration of tool-augmented multimodal reasoning.

📜 Citation

@inproceedings{wang2026ciqiagent,
      title={CiQi-Agent: Aligning Vision, Tools and Aesthetics in Multimodal Agent for Cultural Reasoning on Chinese Porcelains}, 
      author={Wenhan Wang and Zhixiang Zhou and Zhongtian Ma and Yanzhu Chen and Ziyu Lin and Hao Sheng and Pengfei Liu and Honglin Ma and Wenqi Shao and Qiaosheng Zhang and Yu Qiao},
      booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
      year={2026},
      eprint={2603.28474},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2603.28474}, 
}