LLM代理能否高效协作救援？研究揭示其在紧迫性任务中的表现与瓶颈。
Can LLM Agents Solve Collaborative Tasks? A Study on Urgency-Aware Planning and Coordination
- 设计基于图的救援任务，让LLM代理分工协作、按紧急程度优先处理
- 发现代理在高紧迫任务中效率提升32%，但常因资源冲突导致冗余动作
- 适合关注多智能体协作、强化学习与大模型结合的研究者参考
多智能体协同行动能力对解决复杂现实问题至关重要。大型语言模型（LLMs）在沟通、规划和推理方面表现出色，引发其是否能在多智能体场景中实现有效协作的疑问。本文研究了LLM代理在结构化受害者救援任务中的表现，该任务要求分工、优先级判断和协作规划。代理在完全已知的基于图的环境中运作，需为具有不同需求和紧迫性的受害者分配资源。我们通过一系列敏感于协调的指标系统评估其性能，包括任务成功率、冗余动作、房间冲突和紧迫性加权效率。本研究揭示了LLMs在物理基础多智能体协作任务中的优势与失败模式，为未来基准测试与架构改进提供依据。
原文摘要 · Abstract (English)
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.
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