@inproceedings{wang-etal-2026-towards,
    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://preview.aclanthology.org/ingest-acl/2026.acl-1.3/",
    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."
}