Dual Latent Memory for Visual Multi-agent System

2025-04-26 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Computer Vision, Robotics & Autonomous Systems · Depth: Expert, extended

Summary

Visual Multi-Agent Systems (VMAS) currently face a "scaling wall," where increasing agent turns paradoxically degrades performance and exponentially inflates token costs. This issue stems from the information bottleneck inherent in text-centric communication, which causes semantic loss and conflates perceptual and cognitive information. To address this, L2-VMAS, a novel model-agnostic framework, introduces dual latent memories that decouple perception and thinking trajectories. It also employs an entropy-driven proactive triggering mechanism for efficient, on-demand memory access. Extensive experiments across five VLM backbones, four model sizes, and six multi-agent structures demonstrate that L2-VMAS effectively breaks this "scaling wall," improving average accuracy by 2.7-5.4% while reducing token usage by 21.3-44.8%.

Key takeaway

For Machine Learning Engineers developing Visual Multi-Agent Systems, recognize that traditional text-based communication leads to performance degradation and high token costs with increased agent turns. You should consider adopting latent memory architectures like L2-VMAS to improve scalability and cost-efficiency. Explore decoupling perception and thinking memories and implementing proactive, entropy-driven retrieval to overcome the "scaling wall" in your multi-agent designs.

Key insights

Text-centric communication creates a "scaling wall" in VMAS; L2-VMAS overcomes this with decoupled, proactively accessed dual latent memories.

Principles

• Text-centric communication in VMAS creates an information bottleneck.
• Decoupling perception and thinking trajectories improves multi-agent collaboration.
• Proactive, on-demand memory access enhances efficiency over passive transmission.

Method

L2-VMAS dynamically synthesizes dual latent perception and thinking memories. It orchestrates these via an entropy-driven proactive triggering mechanism for on-demand retrieval, keeping base VLMs frozen.

In practice

• Evaluate VMAS performance beyond 3 agent turns to detect the "scaling wall."
• Implement latent memory systems to reduce token costs in multi-agent VLMs.
• Train memory components using a three-stage RL-driven scheme (PPO).

Topics

• Visual Multi-Agent Systems
• Latent Memory
• Information Bottleneck
• Entropy-driven Triggering
• Vision-Language Models
• Scalability
• Token Efficiency

Code references

• YU-deep/L2-VMAS

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.