@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://aclanthology.org/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."
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<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 (\mathcalANN), 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, \mathcalANN surpasses leading multi-agent baselines under the same configurations, showing consistent performance improvements.</abstract>
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%0 Conference Proceedings
%T Self-Evolving Multi-Agent Systems via Textual Backpropagation
%A Ma, Xiaowen
%A Ma, Yunpu
%A Lin, Chenyang
%A Yan, Sikuan
%A Bi, Jinhe
%A Cao, Zixuan
%A Tian, Yijun
%A Tresp, Volker
%A Schuetze, Hinrich
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F ma-etal-2026-self
%X 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 (\mathcalANN), 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, \mathcalANN surpasses leading multi-agent baselines under the same configurations, showing consistent performance improvements.
%U https://aclanthology.org/2026.findings-acl.483/
%P 9918-9951
Markdown (Informal)
[Self-Evolving Multi-Agent Systems via Textual Backpropagation](https://aclanthology.org/2026.findings-acl.483/) (Ma et al., Findings 2026)
ACL
- Xiaowen Ma, Yunpu Ma, Chenyang Lin, Sikuan Yan, Jinhe Bi, Zixuan Cao, Yijun Tian, Volker Tresp, and Hinrich Schuetze. 2026. Self-Evolving Multi-Agent Systems via Textual Backpropagation. In Findings of the Association for Computational Linguistics: ACL 2026, pages 9918–9951, San Diego, California, United States. Association for Computational Linguistics.