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Xuanwu: Evolving General Multimodal Models into an Industrial-Grade Foundation for Content Ecosystems

ArXiv CS.AIby Zhiqian Zhang, Xu Zhao, Xiaoqing Xu, Guangdong Liang, Weijia Wang, Xiaolei Lv, Bo Li, Jun GaoApril 1, 20262 min read0 views

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arXiv:2603.29211v1 Announce Type: new Abstract: In recent years, multimodal large models have continued to improve on general benchmarks. However, in real-world content moderation and adversarial settings, mainstream models still suffer from degraded generalization and catastrophic forgetting because of limited fine-grained visual perception and insufficient modeling of long-tail noise. In this paper, we present Xuanwu VL-2B as a case study of how general multimodal models can be developed into an industrial-grade foundation model for content ecosystems. The model adopts a compact InternViT-300M + MLP + Qwen3 1.7B architecture, balancing fine-grained visual perception, language-semantic alignment, and deployment cost within an approximately 2B-parameter budget. To balance business speciali

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Abstract:In recent years, multimodal large models have continued to improve on general benchmarks. However, in real-world content moderation and adversarial settings, mainstream models still suffer from degraded generalization and catastrophic forgetting because of limited fine-grained visual perception and insufficient modeling of long-tail noise. In this paper, we present Xuanwu VL-2B as a case study of how general multimodal models can be developed into an industrial-grade foundation model for content ecosystems. The model adopts a compact InternViT-300M + MLP + Qwen3 1.7B architecture, balancing fine-grained visual perception, language-semantic alignment, and deployment cost within an approximately 2B-parameter budget. To balance business specialization with the retention of general capabilities, we developed a data iteration and curation mechanism and trained the model through a progressive three-stage pipeline: pre-training, mid-training, and post-training. Ablation studies and offline business evaluations show that Xuanwu VL-2B achieves an average score of 67.90 across seven OpenCompass multimodal metrics (vs. 64.27 for InternVL 3.5 2B), an average recall of 94.38% over seven independent business moderation tasks, and a weighted overall recall of 82.82% on policy-violating text in challenging adversarial OCR scenarios, outperforming Gemini-2.5-Pro (76.72%). These results show that, under a limited parameter budget, Xuanwu VL-2B achieves a practical balance among business alignment, visual perception, general capability retention, and deployment cost.

Comments: 41 pages, 10 figures

Subjects:

Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)

Cite as: arXiv:2603.29211 [cs.AI]

(or arXiv:2603.29211v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2603.29211

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Xiaolei Lv [view email] [v1] Tue, 31 Mar 2026 03:27:49 UTC (4,509 KB)

Original source

ArXiv CS.AI

https://arxiv.org/abs/2603.29211

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