{"slug": "visual-seeker-towards-visual-native-multimodal-agentic-search-via-active-visual", "title": "Visual-Seeker: Towards Visual-Native Multimodal Agentic Search via Active Visual Reasoning", "summary": "Researchers propose Visual-Seeker, a multimodal deep search agent that actively reasons over visual details to perform multi-hop, cross-modal search. The agent achieves state-of-the-art results on five benchmarks, surpassing proprietary models, by using a synthesized dataset of 5K trajectories for training.", "body_md": "arXiv:2606.15231v1 Announce Type: new\nAbstract: Multimodal large language models (MLLMs) have demonstrated impressive capabilities in many visual tasks, but they often struggle with factual grounding when confronted with complex, open-world scenarios. While recent multimodal deep search agents attempt to address this issue by utilizing external tools, the visual-native search paradigm remains underexplored. Existing methods primarily rely on simple images with explicit semantics and text-only evidence trajectories, limiting the agent's ability to perform multi-hop, cross-modal reasoning and search. To address these limitations, we propose Visual-Seeker, a visual-native multimodal deep search agent via active visual reasoning. Rather than treating vision as a static input, our agent actively attends to fine-grained visual details, dynamically harvests visual evidence throughout the search process. To unlock its visual-native potential, we design an active visual reasoning data pipeline and synthesize 5K high-quality multimodal trajectories for model training. Extensive experiments demonstrate the state-of-the-art performance across five challenging multimodal search benchmarks, even surpassing several proprietary models, validating robust visual-native reasoning and search in real-world web environments. The code and data can be accessed at: https://github.com/ZhengboZhang/Visual-Seeker.", "url": "https://wpnews.pro/news/visual-seeker-towards-visual-native-multimodal-agentic-search-via-active-visual", "canonical_source": "https://arxiv.org/abs/2606.15231", "published_at": "2026-06-16 04:00:00+00:00", "updated_at": "2026-06-16 04:21:37.755945+00:00", "lang": "en", "topics": ["artificial-intelligence", "computer-vision", "ai-agents", "large-language-models"], "entities": ["Visual-Seeker", "arXiv", "Zhengbo Zhang"], "alternates": {"html": "https://wpnews.pro/news/visual-seeker-towards-visual-native-multimodal-agentic-search-via-active-visual", "markdown": "https://wpnews.pro/news/visual-seeker-towards-visual-native-multimodal-agentic-search-via-active-visual.md", "text": "https://wpnews.pro/news/visual-seeker-towards-visual-native-multimodal-agentic-search-via-active-visual.txt", "jsonld": "https://wpnews.pro/news/visual-seeker-towards-visual-native-multimodal-agentic-search-via-active-visual.jsonld"}}