@inproceedings{yang-etal-2026-gbc,
    title = "{GBC}: Gradient-Based Connections for Optimizing Multi-Agent Systems",
    author = {Yang, Xiaocheng  and
      Alrabah, Abdulrahman  and
      Hakkani-T{\"u}r, Dilek  and
      Tur, Gokhan},
    editor = "Choi, Jinho D.  and
      Chen, Yun-Nung  and
      Funakoshi, Kotaro  and
      Emami, Ali",
    booktitle = "Proceedings of the 27th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = aug,
    year = "2026",
    address = "Atlanta, Georgia, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-lrec/2026.sigdial-1.24/",
    pages = "342--356",
    abstract = "Multi-agent systems (MAS) built on large language models (LLMs) provide a promising framework for solving complex tasks through role specialization and structured interaction. However, their performance is often limited by miscoordination and, more fundamentally, the lack of fine-grained credit assignment across agents. Existing approaches typically rely on coarse-grained feedback, making it difficult to identify which agents or interaction steps are responsible for errors. We propose Gradient-Based Connections (GBC), an approach for fine-grained attribution and optimization of multi-agent systems. GBC models a MAS as a computational graph and introduces gradient-based connection weights to quantify the influence of each agent{'}s output on downstream agents at the token level. By constructing an attribution graph and propagating task-specific loss signals backward, our method enables precise identification of error sources and targeted prompt optimization. We further develop AgentChord, an efficient implementation that leverages prefix-based gradient computation. Experiments on MultiWOZ and {\ensuremath{\tau}}-bench show that GBC improves multi-agent performance and outperforms strong single-agent and multi-agent baselines, and higher attribution quality is associated with greater optimization effectiveness. Code is available at: https://github.com/yxc-cyber/AgentChord."
}