arXiv:2605.30698v1 Announce Type: cross 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. Existing multi-agent VQA methods predominantly adapt text-centric protocols, focusing on textual discussions while ignoring the alignment of visual information. In this work,
The paper addresses the current limitations of multi-agent collaboration in multimodal AI, specifically the under-exploration of visual alignment, amidst the rapid advancement of vision-language models.
Improving multi-agent visual question answering by incorporating visual evidence directly into consensus-building can significantly reduce AI hallucinations and enhance the reliability of agentic systems.
Current text-centric multi-agent VQA protocols will evolve to include dedicated visual alignment mechanisms, leading to more robust and trustworthy multimodal AI agents.
- · AI agents developers
- · Multimodal AI platforms
- · Companies using VQA for critical applications
- · Computer vision researchers
- · Systems heavily reliant on text-only agent collaboration
- · AI applications prone to visual hallucination
- · Agents lacking sophisticated visual reasoning
Multi-agent systems will achieve higher accuracy and reduce errors in visual understanding tasks.
Enhanced reliability could accelerate the deployment of AI agents in sensitive domains like diagnostics or autonomous systems.
This could lead to a broader societal adoption of AI, as trust in AI's perception and reasoning improves significantly.
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Read at arXiv cs.AI