arXiv:2602.15382v2 Announce Type: replace-cross Abstract: Multi-Agent Systems (MAS) powered by Large Language Models have unlocked advanced collaborative reasoning, yet they remain bottlenecked by discrete text communication, which imposes runtime overhead and information quantization loss. While latent state transfer offers an alternative, existing approaches either assume homogeneous sender--receiver architectures or rely on pair-specific learned translators, limiting scalability across diverse model families with disjoint manifolds. We reconceptualize the visual interface of Vision-Language
The rapid advancement of Large Language Models has necessitated more efficient communication methods within multi-agent systems to overcome current bottlenecks.
This development addresses a critical limitation in AI agent systems, enabling more scalable and efficient collaboration across diverse AI architectures.
Communication between heterogeneous AI agents can move beyond discrete text to more efficient latent-space transfers, reducing overhead and information loss.
- · AI agent developers
- · Multi-agent system platforms
- · Generative AI companies
- · Software automation sector
- · Legacy text-based communication protocols in AI
- · Inefficient multi-agent system architectures
Improved performance and scalability of multi-agent AI systems, particularly those integrating disparate models.
Acceleration of complex AI applications, leading to more sophisticated automation and problem-solving capabilities.
Enhanced AI 'teamwork' that could enable autonomous systems to tackle highly intricate, real-world challenges more effectively.
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Read at arXiv cs.LG