@inproceedings{jiang-etal-2026-dynamic,
    title = "Dynamic Generation of Multi {LLM} Agents Communication Topologies with Graph Diffusion Models",
    author = "Jiang, Eric Hanchen  and
      Li, Levina  and
      Wan, Frank  and
      Liang, Xiao  and
      Yin, Sophia  and
      Wu, Yuchen  and
      Li, Xinfeng  and
      Sun, Yizhou  and
      Wang, Wei  and
      Chang, Kai-Wei  and
      Wu, Ying Nian",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.1764/",
    pages = "38042--38060",
    ISBN = "979-8-89176-390-6",
    abstract = "The efficiency of multi-agent systems driven by large language models (LLMs) largely hinges on their communication topology. However, designing an optimal topology is a non-trivial challenge, as it requires balancing competing objectives such as task performance, communication cost, and robustness. Existing frameworks often rely on static or hand-crafted topologies, which inherently fail to adapt to diverse task requirements, leading to either excessive token consumption for simple problems or performance bottlenecks for complex ones. To address this challenge, we introduce a novel generative framework called \textit{Guided Topology Diffusion (GTD)}. Inspired by conditional discrete graph diffusion models, GTD formulates topology synthesis as an iterative construction process. At each step, the generation is steered by a lightweight proxy model that predicts multi-objective rewards (e.g., accuracy, utility, cost), enabling real-time, gradient-free optimization towards task-adaptive topologies. This iterative, guided synthesis process distinguishes GTD from single-step generative frameworks, enabling it to better navigate complex design trade-offs. We validated GTD across multiple benchmarks, and experiments show that this framework can generate highly task-adaptive, sparse, and efficient communication topologies, significantly outperforming existing methods in LLM agent collaboration. Our code is available at \url{https://anonymous.4open.science/r/diffusion_agent-953C}."
}