Can LLM Agents Solve Collaborative Tasks? A Study on Urgency-Aware Planning and Coordination
This addresses the challenge of multi-agent coordination for real-world problem-solving, but it appears incremental as it builds on existing LLM capabilities without introducing a new paradigm.
The study tackled the problem of whether LLM agents can effectively coordinate in multi-agent collaborative tasks, specifically a victim rescue scenario with urgency-aware planning, and found that systematic evaluation revealed strengths and failure modes in their performance, though no concrete numbers were provided in the abstract.
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.