@inproceedings{wang-etal-2026-towards-effective,
title = "Towards Effective and Efficient Multi-Agent Language Model Systems: Foundations, Prospects, and Applications",
author = "Wang, Xuan and
Cao, Shuxiang and
Zhuang, Yuchen and
Shi, Wenqi",
editor = "Andreas, Jacob and
Murray, Kenton",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 5: Tutorial Abstracts)",
month = jul,
year = "2026",
address = "San Diego, California, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-tutorials.3/",
pages = "5--6",
ISBN = "979-8-89176-394-4",
abstract = "Multi-agent systems powered by large language models (LLMs) offer a promising paradigm for tackling complex reasoning, decision-making, and problem-solving tasks. However, achieving both effectiveness and efficiency in such systems remains a critical challenge. This tutorial introduces recent advances in building effective and efficient multi-agent LLM systems, focusing on three core components. First, we discuss the design of individual LLM agents. We present state-of-the-art techniques for enabling capable agents using efficient and compact LLMs, including model distillation, dynamic routing, and memory- and compute efficient serving, providing a foundation for scalable and responsive agent design under resource constraints. Second, we cover coordination and communication among agents, crucial for collective performance, highlighting methods for improving multi-agent reasoning and decision-making through prompt and graph optimization, sycophancy mitigation, and structured LLM-based frameworks. Last, we explore real-world applications of LLM agents in areas such as industry, healthcare, quantum computing, and various scientific domains."
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%0 Conference Proceedings
%T Towards Effective and Efficient Multi-Agent Language Model Systems: Foundations, Prospects, and Applications
%A Wang, Xuan
%A Cao, Shuxiang
%A Zhuang, Yuchen
%A Shi, Wenqi
%Y Andreas, Jacob
%Y Murray, Kenton
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 5: Tutorial Abstracts)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-394-4
%F wang-etal-2026-towards-effective
%X Multi-agent systems powered by large language models (LLMs) offer a promising paradigm for tackling complex reasoning, decision-making, and problem-solving tasks. However, achieving both effectiveness and efficiency in such systems remains a critical challenge. This tutorial introduces recent advances in building effective and efficient multi-agent LLM systems, focusing on three core components. First, we discuss the design of individual LLM agents. We present state-of-the-art techniques for enabling capable agents using efficient and compact LLMs, including model distillation, dynamic routing, and memory- and compute efficient serving, providing a foundation for scalable and responsive agent design under resource constraints. Second, we cover coordination and communication among agents, crucial for collective performance, highlighting methods for improving multi-agent reasoning and decision-making through prompt and graph optimization, sycophancy mitigation, and structured LLM-based frameworks. Last, we explore real-world applications of LLM agents in areas such as industry, healthcare, quantum computing, and various scientific domains.
%U https://aclanthology.org/2026.acl-tutorials.3/
%P 5-6
Markdown (Informal)
[Towards Effective and Efficient Multi-Agent Language Model Systems: Foundations, Prospects, and Applications](https://aclanthology.org/2026.acl-tutorials.3/) (Wang et al., ACL 2026)
ACL