arXiv:2506.09046v3 Announce Type: replace-cross Abstract: Leveraging multiple Large Language Models (LLMs) has proven effective for addressing complex, high-dimensional tasks, but current approaches often rely on static, manually engineered multi-agent configurations. To overcome these constraints, we present the Agentic Neural Network (ANN), a framework that conceptualizes multi-agent collaboration as a layered neural network architecture. In this design, each agent operates as a node, and each layer forms a cooperative team focused on a specific subtask. Our framework follows a two-phase opt
The rapid advancement and limitations of current LLM-based multi-agent systems necessitate more sophisticated, self-organizing architectures to handle greater complexity.
This development proposes a framework that could significantly enhance the autonomy and adaptability of AI agents, moving beyond manually engineered configurations to self-evolving systems.
AI multi-agent systems can potentially transition from static designs to dynamic, self-configuring architectures analogous to neural networks, improving their problem-solving capabilities.
- · AI software developers
- · Enterprises adopting AI agents
- · LLM providers
- · Companies reliant on simple, static AI automation
This research suggests a more scalable and robust method for deploying AI agents in complex environments.
The ability of agents to self-evolve could lead to unexpected emergent behaviors and capabilities, accelerating automation in various sectors.
As agentic systems become more autonomous and self-adaptive, they could rapidly collapse multiple layers of traditional SaaS and white-collar workflows.
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Read at arXiv cs.AI