Seeing Before Agreeing: Aligning Multi-Agent Consensus with Visual Evidence 事件

Seeing Before Agreeing: Aligning Multi-Agent Consensus with Visual Evidence arXiv:2605.30698v1 Announce Type: new Abstract: 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.