AI RESEARCH
Seeing Before Agreeing: Aligning Multi-Agent Consensus with Visual Evidence
arXiv CS.AI
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ArXi:2605.30698v1 Announce Type: cross Vision-language models (VLMs) have achieved strong performance on visual question answering (VQA). To mitigate individual hallucinations and blind spots, aggregating diverse perspectives via multi-agent collaboration has emerged as a promising paradigm. While this approach has shown great success in textual QA, its potential in the multimodal domain remains under-explored. Existing multi-agent VQA methods predominantly adapt text-centric protocols, focusing on textual discussions while ignoring the alignment of visual information.