AI RESEARCH
ROVER: Routing Object-Centric Visual Evidence for Grounded Multi-Image Reasoning
arXiv CS.AI
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ArXi:2605.27959v1 Announce Type: cross Multimodal Large Language Models (MLLMs) have increasingly localized and interleaved visual evidence for deliberative reasoning. Grounding-based approaches typically focus on regions of interest (RoIs) by injecting cropped image patches or RoI-specific features into the reasoning context. However, such designs can weaken holistic scene understanding and inter-object relations, while incurring decoding costs that scale with the number and size of RoIs.