arXiv:2602.00471v2 Announce Type: replace Abstract: While Visual Multi-Agent Systems (VMAS) promise to enhance comprehensive abilities through inter-agent collaboration, empirical evidence reveals a counter-intuitive "scaling wall": increasing agent turns often degrades performance while exponentially inflating token costs. We attribute this failure to the information bottleneck inherent in text-centric communication, where converting perceptual and thinking trajectories into discrete natural language inevitably induces semantic loss. To this end, we propose \textbf{L}$\mathbf{^{2}}$\textbf{-V
The proliferation of multi-agent AI systems has highlighted the inherent limitations of current communication paradigms, leading researchers to address the 'scaling wall' caused by information bottlenecks.
This research addresses a critical limitation in visual multi-agent systems, potentially enabling more efficient and complex AI behaviors crucial for various applications.
The proposed 'Dual Latent Memory' could fundamentally alter how AI agents communicate and collaborate, moving beyond text-centric methods to reduce semantic loss and improve performance.
- · AI developers
- · Robotics industry
- · Generative AI platforms
- · Inefficient multi-agent system architectures
- · Organizations reliant on simple, text-based AI communication
Improved performance and scalability of visual multi-agent systems will lead to more robust autonomous AI applications.
Enhanced multi-agent collaboration could accelerate the development of complex AI agents that can handle highly dynamic and visual tasks.
This could contribute to the realization of general-purpose AI agents capable of collapsing white-collar workflows and driving new forms of automation.
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Read at arXiv cs.AI