DynTaskMAS: A Dynamic Task Graph-driven Framework for Asynchronous and Parallel LLM-based Multi-Agent Systems

• URL: http://arxiv.org/abs/2503.07675v1
• Date: Mon, 10 Mar 2025 06:16:10 GMT
• Title: DynTaskMAS: A Dynamic Task Graph-driven Framework for Asynchronous and Parallel LLM-based Multi-Agent Systems
• Authors: Junwei Yu, Yepeng Ding, Hiroyuki Sato,
• Abstract summary: This paper introduces DynTaskMAS, a novel framework that orchestrates asynchronous and parallel operations in Multi-Agent Systems.<n>The framework features four key innovations: (1) a Dynamic Task Graph Generator that decomposes complex tasks while maintaining logical dependencies, (2) an Asynchronous Parallel Execution Engine that optimize resource utilization through efficient task scheduling, and (3) a Semantic-Aware Context Management System that enables efficient information sharing among agents.
• Score: 2.6353853440763113
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The emergence of Large Language Models (LLMs) in Multi-Agent Systems (MAS) has opened new possibilities for artificial intelligence, yet current implementations face significant challenges in resource management, task coordination, and system efficiency. While existing frameworks demonstrate the potential of LLM-based agents in collaborative problem-solving, they often lack sophisticated mechanisms for parallel execution and dynamic task management. This paper introduces DynTaskMAS, a novel framework that orchestrates asynchronous and parallel operations in LLM-based MAS through dynamic task graphs. The framework features four key innovations: (1) a Dynamic Task Graph Generator that intelligently decomposes complex tasks while maintaining logical dependencies, (2) an Asynchronous Parallel Execution Engine that optimizes resource utilization through efficient task scheduling, (3) a Semantic-Aware Context Management System that enables efficient information sharing among agents, and (4) an Adaptive Workflow Manager that dynamically optimizes system performance. Experimental evaluations demonstrate that DynTaskMAS achieves significant improvements over traditional approaches: a 21-33% reduction in execution time across task complexities (with higher gains for more complex tasks), a 35.4% improvement in resource utilization (from 65% to 88%), and near-linear throughput scaling up to 16 concurrent agents (3.47X improvement for 4X agents). Our framework establishes a foundation for building scalable, high-performance LLM-based multi-agent systems capable of handling complex, dynamic tasks efficiently.

Related papers

• OmniNova:A General Multimodal Agent Framework [0.5439020425819]
  Large Language Models (LLMs) with specialized tools presents new opportunities for intelligent automation systems. We present OmniNova, a modular multi-agent automation framework that combines language models with specialized tools such as web search, crawling, and code execution capabilities.
  arXiv  Detail & Related papers  (2025-03-25T19:21:01Z)
• Enhancing Multi-Agent Systems via Reinforcement Learning with LLM-based Planner and Graph-based Policy [31.041340552853004]
  Graph Collaboration MARL (LGC-MARL) is a framework that efficiently combines Large Language Models (LLMs) and Multi-Agent Reinforcement Learning (MARL) LGC-MARL decomposes complex tasks into executable subtasks and achieves efficient collaboration among multiple agents through graph-based coordination. Experimental results on the AI2-THOR simulation platform demonstrate the superior performance and scalability of LGC-MARL.
  arXiv  Detail & Related papers  (2025-03-13T05:02:49Z)
• Parallelized Planning-Acting for Efficient LLM-based Multi-Agent Systems [31.894636711684523]
  We propose a novel parallelized planning-acting framework for Multi-Agent Systems.<n>The proposed framework features a dual-thread architecture with interruptible execution to enable concurrent planning and acting.
  arXiv  Detail & Related papers  (2025-03-05T13:53:10Z)
• Autonomous Deep Agent [0.7489814067742621]
  Deep Agent is an advanced autonomous AI system designed to manage complex multi-phase tasks.<n>The system's foundation is built on our Hierarchical Task DAG framework.<n>Deep Agent establishes a novel paradigm in self-governing AI systems.
  arXiv  Detail & Related papers  (2025-02-10T21:46:54Z)
• Flow: Modularized Agentic Workflow Automation [53.073598156915615]
  Multi-agent frameworks powered by large language models (LLMs) have demonstrated great success in automated planning and task execution.<n>However, the effective adjustment of agentic during execution has not been well studied.<n>In this paper, we define an activity-on-vertex (AOV) graph, which allows continuous workflow refinement by agents.<n>Our proposed multi-agent framework achieves efficient concurrent execution of subtasks, effective goal achievement, and enhanced error tolerance.
  arXiv  Detail & Related papers  (2025-01-14T04:35:37Z)
• MaCTG: Multi-Agent Collaborative Thought Graph for Automatic Programming [10.461509044478278]
  MaCTG (MultiAgent Collaborative Thought Graph) is a novel multi-agent framework that employs a dynamic graph structure. It autonomously assigns agent roles based on programming requirements, dynamically refines task distribution, and systematically verifies and integrates project-level code. MaCTG significantly reduced operational costs by 89.09% compared to existing multi-agent frameworks.
  arXiv  Detail & Related papers  (2024-10-25T01:52:15Z)
• TaskBench: Benchmarking Large Language Models for Task Automation [82.2932794189585]
  We introduce TaskBench, a framework to evaluate the capability of large language models (LLMs) in task automation. Specifically, task decomposition, tool selection, and parameter prediction are assessed. Our approach combines automated construction with rigorous human verification, ensuring high consistency with human evaluation.
  arXiv  Detail & Related papers  (2023-11-30T18:02:44Z)
• ADaPT: As-Needed Decomposition and Planning with Language Models [131.063805299796]
  We introduce As-Needed Decomposition and Planning for complex Tasks (ADaPT) ADaPT explicitly plans and decomposes complex sub-tasks as-needed, when the Large Language Models is unable to execute them. Our results demonstrate that ADaPT substantially outperforms established strong baselines.
  arXiv  Detail & Related papers  (2023-11-08T17:59:15Z)
• A Dynamic LLM-Powered Agent Network for Task-Oriented Agent Collaboration [55.35849138235116]
  We propose automatically selecting a team of agents from candidates to collaborate in a dynamic communication structure toward different tasks and domains. Specifically, we build a framework named Dynamic LLM-Powered Agent Network ($textDyLAN$) for LLM-powered agent collaboration. We demonstrate that DyLAN outperforms strong baselines in code generation, decision-making, general reasoning, and arithmetic reasoning tasks with moderate computational cost.
  arXiv  Detail & Related papers  (2023-10-03T16:05:48Z)
• Controllable Dynamic Multi-Task Architectures [92.74372912009127]
  We propose a controllable multi-task network that dynamically adjusts its architecture and weights to match the desired task preference as well as the resource constraints. We propose a disentangled training of two hypernetworks, by exploiting task affinity and a novel branching regularized loss, to take input preferences and accordingly predict tree-structured models with adapted weights.
  arXiv  Detail & Related papers  (2022-03-28T17:56:40Z)
• MALib: A Parallel Framework for Population-based Multi-agent Reinforcement Learning [61.28547338576706]
  Population-based multi-agent reinforcement learning (PB-MARL) refers to the series of methods nested with reinforcement learning (RL) algorithms. We present MALib, a scalable and efficient computing framework for PB-MARL.
  arXiv  Detail & Related papers  (2021-06-05T03:27:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.