2024/12/16 by Timothée Anne, Noah Syrkis, Meriem Elhosni +4 · 1 voice
Computer Science · #Multi-Agent Systems and Negotiation #cs.AI
paper · pdf · doi:10.1109/tg.2025.3564042
arxiv published 2024/12/16 · arxiv updated 2025/04/22 · openalex publication_date 2025/04/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/06/11
Large Language Models (LLMs) have demonstrated remarkable performance across various tasks. Their potential to facilitate human coordination with many agents is a promising but largely under-explored area. Such capabilities would be helpful in disaster response, urban planning, and real-time strategy scenarios. In this work, we introduce (1) a real-time strategy game benchmark designed to evaluate these abilities and (2) a novel framework we term HIVE. HIVE empowers a single human to coordinate swarms of up to 2,000 agents through a natural language dialog with an LLM. We present promising results on this multi-agent benchmark, with our hybrid approach solving tasks such as coordinating agent movements, exploiting unit weaknesses, leveraging human annotations, and understanding terrain and strategic points. Our findings also highlight critical limitations of current models, including difficulties in processing spatial visual information and challenges in formulating long-term strategic plans. This work sheds light on the potential and limitations of LLMs in human-swarm coordination, paving the way for future research in this area. The HIVE project page, hive.syrkis.com, includes videos of the system in action.