Agentic Neural Networks: Self-Evolving Multi-Agent Systems via Textual Backpropagation

• URL: http://arxiv.org/abs/2506.09046v1
• Date: Tue, 10 Jun 2025 17:59:21 GMT
• Title: Agentic Neural Networks: Self-Evolving Multi-Agent Systems via Textual Backpropagation
• Authors: Xiaowen Ma, Chenyang Lin, Yao Zhang, Volker Tresp, Yunpu Ma,
• Abstract summary: We present a framework that conceptualizes multi-agent collaboration as a layered neural network architecture.<n>In this design, each agent operates as a node, and each layer forms a cooperative "team" focused on a specific subtask.<n>Our findings indicate that ANN provides a scalable, data-driven framework for multi-agent systems.
• Score: 29.45297422127962
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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. Agentic Neural Network follows a two-phase optimization strategy: (1) Forward Phase-Drawing inspiration from neural network forward passes, tasks are dynamically decomposed into subtasks, and cooperative agent teams with suitable aggregation methods are constructed layer by layer. (2) Backward Phase-Mirroring backpropagation, we refine both global and local collaboration through iterative feedback, allowing agents to self-evolve their roles, prompts, and coordination. This neuro-symbolic approach enables ANN to create new or specialized agent teams post-training, delivering notable gains in accuracy and adaptability. Across four benchmark datasets, ANN surpasses leading multi-agent baselines under the same configurations, showing consistent performance improvements. Our findings indicate that ANN provides a scalable, data-driven framework for multi-agent systems, combining the collaborative capabilities of LLMs with the efficiency and flexibility of neural network principles. We plan to open-source the entire framework.

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