arXiv:2511.20639v3 Announce Type: replace Abstract: Multi-agent systems (MAS) extend large language models (LLMs) from independent single-model reasoning to coordinative system-level intelligence. While existing LLM agents depend on text-based mediation for reasoning and communication, we take a step forward by enabling models to collaborate directly within the continuous latent space. We introduce LatentMAS, an end-to-end training-free framework that enables pure latent collaboration among LLM agents. In LatentMAS, each agent first performs auto-regressive latent thoughts generation through l
The rapid advancement of large language models is opening new avenues for autonomous multi-agent systems, moving beyond text-based communication to more integrated forms of collaboration.
Pure latent collaboration in AI agents could lead to significantly more efficient and powerful autonomous systems, impacting workflow automation and the capabilities of AI-driven entities.
AI agents are moving from explicit, text-mediated communication to implicit, continuous latent space collaboration, potentially enabling more seamless and complex multi-agent interactions.
- · AI software developers
- · Cloud computing providers
- · Enterprises adopting AI agents
- · Tasks requiring manual coordination
- · Legacy workflow automation platforms
Increased efficiency and autonomy in complex AI applications through sophisticated multi-agent interactions.
Accelerated development of AI systems capable of tackling problems previously considered beyond current AI capabilities.
Ethical considerations and control mechanisms for highly autonomous, self-coordinating AI systems become even more critical.
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