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June 6, 2026Electronics0 citationsOpen Access

Dynamic Task Graph Approach for Scalable Collaboration in LLM-based Multi-Agent Systems

Toward Scalable LLM-Based Multi-Agent Collaboration: A Dynamic Task Graph Approach with Asynchronous Parallel Execution

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Authors

JYJunwei YuThe University of TokyoYDYepeng DingHiroshima UniversityJDJiani DaiHiroshima University

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Overview

Randomized trial evaluates execution efficiency in LLM-based multi-agent systems, highlighting enhancements for scalability.

Key Points

  • This paper aims to improve coordination and efficiency in large language model-based multi-agent systems (MAS) through a new framework.
  • Introduces DynTaskMAS, a framework using dynamic task graphs for LLM-based MASs.
  • Develops a runtime task decomposition module to capture evolving task dependencies.
  • Implements a self-tuning workflow controller to adapt execution priorities.
  • Achieves a 21.3–33.0% reduction in execution time compared to sequential models.
  • Increases resource utilization from 65% to 88%.
  • Demonstrates a 3.47× throughput increase with 16 concurrent agents.

Cite This Study

Yu et al. (2026) studied this question.

synapsesocial.com/papers/6a23bbeb71a5da9775e774c1https://doi.org/10.3390/electronics15112475
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