中文标题#
LLM 代理能解決協作任務嗎？ 關於緊急意識規劃與協調的研究
英文标题#
Can LLM Agents Solve Collaborative Tasks? A Study on Urgency-Aware Planning and Coordination
中文摘要#
協調多個智能體行動的能力對於解決複雜的真實世界問題至關重要。 大型語言模型（LLMs）在通信、規劃和推理方面表現出強大的能力，這引發了是否它們也能在多智能體環境中支持有效協作的問題。 在本工作中，我們研究了使用 LLM 智能體解決需要分工、優先級和合作規劃的結構化受害者救援任務。 智能體在一個完全已知的基於圖的環境中運行，並必須將資源分配給需求和緊急程度不同的受害者。 我們使用一系列對協調敏感的指標系統地評估其性能，包括任務成功率、冗餘動作、房間衝突和緊急程度加權效率。 這項研究為 LLM 在物理基礎的多智能體協作任務中的優勢和失敗模式提供了新的見解，有助於未來基準測試和架構改進。
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 代理能解決協作任務嗎？ 關於緊急意識規劃與協調的研究
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