Visual-Seeker: Towards Visual-Native Multimodal Agentic Search via Active Visual Reasoning

2026-06-13 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Emerging Technologies & Innovation · Depth: Expert, quick

Summary

Visual-Seeker is a novel visual-native multimodal deep search agent designed to overcome the factual grounding limitations of multimodal large language models (MLLMs) in complex, open-world scenarios. Unlike existing multimodal deep search agents that often rely on simple images and text-only evidence, Visual-Seeker actively attends to fine-grained visual details and dynamically harvests visual evidence throughout the search process. To enable its visual-native capabilities, the agent utilizes an active visual reasoning data pipeline and was trained on 5K high-quality synthesized multimodal trajectories. Extensive experiments demonstrate that Visual-Seeker achieves impressive performance across five challenging multimodal search benchmarks, even surpassing several proprietary models, validating its robust visual-native reasoning and search in real-world web environments. The code and data are publicly accessible.

Key takeaway

For AI Scientists developing multimodal agents, Visual-Seeker's approach suggests a critical shift from static image input to active visual reasoning. You should explore integrating dynamic visual evidence harvesting into your search agents to improve factual grounding in complex scenarios. Consider using synthetic multimodal trajectories for training to enable robust visual-native capabilities, potentially surpassing current proprietary models in performance.

Key insights

Visual-Seeker introduces active visual reasoning for multimodal search, dynamically using fine-grained visual evidence.

Principles

• Vision should be an active, not static, input.
• Dynamic visual evidence improves factual grounding.
• Synthesized trajectories enhance visual-native potential.

Method

Visual-Seeker employs an active visual reasoning data pipeline to synthesize 5K high-quality multimodal trajectories, enabling dynamic harvesting of visual evidence during search.

In practice

• Develop agents that actively process visual details.
• Create synthetic multimodal training trajectories.
• Evaluate against diverse multimodal search benchmarks.

Topics

• Multimodal Agents
• Visual Reasoning
• Deep Search
• Large Language Models
• Factual Grounding
• Web Environments

Code references

• ZhengboZhang/Visual-Seeker

Best for: Research Scientist, Computer Vision Engineer, AI Scientist, Machine Learning Engineer, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.