@inproceedings{ma-etal-2026-self,
    title = "Self-Evolving Multi-Agent Systems via Textual Backpropagation",
    author = "Ma, Xiaowen  and
      Ma, Yunpu  and
      Lin, Chenyang  and
      Yan, Sikuan  and
      Bi, Jinhe  and
      Cao, Zixuan  and
      Tian, Yijun  and
      Tresp, Volker  and
      Schuetze, Hinrich",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.483/",
    pages = "9918--9951",
    ISBN = "979-8-89176-395-1",
    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 ($\mathcal{ANN}$), 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. The proposed framework 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 our framework to create new or specialized agent teams post-training, delivering notable gains in accuracy and adaptability. Across seven benchmark datasets, $\mathcal{ANN}$ surpasses leading multi-agent baselines under the same configurations, showing consistent performance improvements."
}