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    <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 \ensuremathτ-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.</abstract>
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%0 Conference Proceedings
%T GBC: Gradient-Based Connections for Optimizing Multi-Agent Systems
%A Yang, Xiaocheng
%A Alrabah, Abdulrahman
%A Hakkani-Tür, Dilek
%A Tur, Gokhan
%Y Choi, Jinho D.
%Y Chen, Yun-Nung
%Y Funakoshi, Kotaro
%Y Emami, Ali
%S Proceedings of the 27th Annual Meeting of the Special Interest Group on Discourse and Dialogue
%D 2026
%8 August
%I Association for Computational Linguistics
%C Atlanta, Georgia, USA
%F yang-etal-2026-gbc
%X 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 \ensuremathτ-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.
%U https://aclanthology.org/2026.sigdial-1.24/
%P 342-356

Markdown (Informal)

[GBC: Gradient-Based Connections for Optimizing Multi-Agent Systems](https://aclanthology.org/2026.sigdial-1.24/) (Yang et al., SIGDIAL 2026)

• GBC: Gradient-Based Connections for Optimizing Multi-Agent Systems (Yang et al., SIGDIAL 2026)

ACL

• Xiaocheng Yang, Abdulrahman Alrabah, Dilek Hakkani-Tür, and Gokhan Tur. 2026. GBC: Gradient-Based Connections for Optimizing Multi-Agent Systems. In Proceedings of the 27th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 342–356, Atlanta, Georgia, USA. Association for Computational Linguistics.