AI RESEARCH
VistaHop: Benchmarking Multi-hop Visual Reasoning for Visual DeepSearch
arXiv CS.CL
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ArXi:2606.03273v1 Announce Type: cross Visual DeepSearch requires multimodal large reasoning model (MLRM) agents to answer complex visual queries by repeatedly inspecting image regions, grounding intermediate reasoning in visual evidence, and connecting fine-grained clues across long reasoning chains. However, existing benchmarks mainly focus on single-step visual understanding or static image-question answering, offering limited evaluation of iterative image inspection, visual-anchor grounding, and multi-hop evidence integration. In this work, we