Dual Latent Memory for Visual Multi-agent System

• URL: http://arxiv.org/abs/2602.00471v1
• Date: Sat, 31 Jan 2026 02:49:10 GMT
• Title: Dual Latent Memory for Visual Multi-agent System
• Authors: Xinlei Yu, Chengming Xu, Zhangquan Chen, Bo Yin, Cheng Yang, Yongbo He, Yihao Hu, Jiangning Zhang, Cheng Tan, Xiaobin Hu, Shuicheng Yan,
• Abstract summary: Visual Multi-Agent Systems (VMAS) promise to enhance comprehensive abilities through inter-agent collaboration.<n>Increasing agent turns often degrades performance while exponentially inflating token costs.<n>We propose L$2$-VMAS, a novel model-agnostic framework that enables inter-agent collaboration with dual latent memories.
• Score: 69.29799381195592
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 L$^{2}$-VMAS, a novel model-agnostic framework that enables inter-agent collaboration with dual latent memories. Furthermore, we decouple the perception and thinking while dynamically synthesizing dual latent memories. Additionally, we introduce an entropy-driven proactive triggering that replaces passive information transmission with efficient, on-demand memory access. Extensive experiments among backbones, sizes, and multi-agent structures demonstrate that our method effectively breaks the "scaling wall" with superb scalability, improving average accuracy by 2.7-5.4% while reducing token usage by 21.3-44.8%. Codes: https://github.com/YU-deep/L2-VMAS.

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