中文标题#
LLM 代理能解决协作任务吗？ 关于紧急意识规划与协调的研究
英文标题#
Can LLM Agents Solve Collaborative Tasks? A Study on Urgency-Aware Planning and Coordination
中文摘要#
协调多个智能体行动的能力对于解决复杂的真实世界问题至关重要。 大型语言模型（LLMs）在通信、规划和推理方面表现出强大的能力，这引发了是否它们也能在多智能体环境中支持有效协作的问题。 在本工作中，我们研究了使用 LLM 智能体解决需要分工、优先级和合作规划的结构化受害者救援任务。 智能体在一个完全已知的基于图的环境中运行，并必须将资源分配给需求和紧急程度不同的受害者。 我们使用一系列对协调敏感的指标系统地评估其性能，包括任务成功率、冗余动作、房间冲突和紧急程度加权效率。 这项研究为 LLM 在物理基础的多智能体协作任务中的优势和失败模式提供了新的见解，有助于未来基准测试和架构改进。
英文摘要#
The ability to coordinate actions across multiple agents is critical for solving complex, real-world problems. Large Language Models (LLMs) have shown strong capabilities in communication, planning, and reasoning, raising the question of whether they can also support effective collaboration in multi-agent settings. In this work, we investigate the use of LLM agents to solve a structured victim rescue task that requires division of labor, prioritization, and cooperative planning. Agents operate in a fully known graph-based environment and must allocate resources to victims with varying needs and urgency levels. We systematically evaluate their performance using a suite of coordination-sensitive metrics, including task success rate, redundant actions, room conflicts, and urgency-weighted efficiency. This study offers new insights into the strengths and failure modes of LLMs in physically grounded multi-agent collaboration tasks, contributing to future benchmarks and architectural improvements.
文章页面#
LLM 代理能解决协作任务吗？ 关于紧急意识规划与协调的研究
PDF 获取#
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