Visual-Seeker: Towards Visual-Native Multimodal Agentic Search via Active Visual Reasoning
Researchers have introduced Visual-Seeker, a novel agent designed for multimodal deep search that prioritizes visual information. Unlike previous methods that treat vision as static input, Visual-Seeker actively engages with fine-grained visual details throughout the search process. This approach aims to enhance multi-hop, cross-modal reasoning in complex web environments. The system has demonstrated state-of-the-art performance on five multimodal search benchmarks, outperforming some proprietary models. AI
IMPACT Enhances multimodal search capabilities by prioritizing active visual reasoning over static image inputs.
- Hugging Face
- DagsHub
- Multimodal Large Language Models and Tunings: Vision, Language, Sensors, Audio, and Beyond
- alphaXiv
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- Visual-Seeker
- Visual-native multimodal deep search agent
- Active visual reasoning
- Web environments