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Can LLM Agents Solve Collaborative Tasks? A Study on Urgency-Aware Planning and Coordination

Created by
  • Haebom

Author

Jo ao Vitor de Carvalho Silva, Douglas G. Macharet

Outline

This paper studies the use of Large Language Model (LLM) agents to solve structured victim rescue tasks in multi-agent environments. LLM agents operate in a graph-based environment requiring labor division, prioritization, and collaborative planning, and must allocate resources to victims with varying needs and urgency levels. We systematically evaluate performance using various collaboration-sensitive metrics, including task success rate, duplicate work, room collisions, and urgency-weighted efficiency. This study provides new insights into the strengths and failure modes of LLM in physically based multi-agent collaborative tasks, contributing to future benchmarks and architecture improvements.

Takeaways, Limitations

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Takeaways:
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LLM demonstrates the potential to perform complex tasks such as division of labor, prioritization, and collaborative planning in multi-agent collaborative tasks.
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The proposed collaboration-sensitive metrics provide a useful framework for evaluating the performance of LLM-based multi-agent systems.
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The findings identify the strengths and limitations of the LLM and suggest directions for future architectural improvements.
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Limitations:
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Research is limited to graph-based, fully known environments and may not fully reflect the uncertainty and complexity of the real world.
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Because the tasks used have a specific structure, generalization to other types of multi-agent tasks may be limited.
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Lack of details about the training and setup of the LLM agent may lead to difficulties in reproducibility.
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