arXiv:2605.15706v2 Announce Type: replace Abstract: Recent advances in Large Language Models (LLMs) have catalyzed the development of multi-agent systems (MAS) for complex reasoning tasks. However, existing MAS typically rely on pre-defined or pre-compiled communication topologies, which limits their flexibility and adaptability to dynamic task requirements. In this work, we propose Differentiable Mixture-of-Agents (DMoA), a self-evolving multi-agent framework that enables elastic and adaptive agent collaboration during inference. Instead of statically constructing workflows, DMoA dynamically
The rapid advancements in large language models necessitate more sophisticated and adaptive multi-agent systems to tackle complex reasoning tasks beyond static, pre-defined architectures.
Improving the flexibility and adaptability of AI agent collaboration directly impacts the potential for autonomous systems to handle dynamic, real-world problems and collapse white-collar workflows.
AI agent systems can now dynamically self-organize their communication and collaboration, moving beyond rigid, pre-configured topologies to more 'swarm-like' intelligence.
- · AI software developers
- · Enterprises adopting AI automation
- · Research institutions in AI/ML
- · Platforms reliant on static workflow orchestration
- · Manual white-collar tasks
- · Companies slow to integrate advanced AI agents
More robust and adaptable AI agents emerge, capable of addressing more complex and dynamic problems.
The efficiency and scope of AI automation increase significantly, potentially disrupting various professional service sectors.
The development of truly autonomous digital entities accelerates, blurring lines between human and machine operational capabilities.
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