Assemble Your Crew: Automatic Multi-agent Communication Topology Design via Autoregressive Graph Generation

• URL: http://arxiv.org/abs/2507.18224v2
• Date: Sat, 26 Jul 2025 01:49:31 GMT
• Title: Assemble Your Crew: Automatic Multi-agent Communication Topology Design via Autoregressive Graph Generation
• Authors: Shiyuan Li, Yixin Liu, Qingsong Wen, Chengqi Zhang, Shirui Pan,
• Abstract summary: Multi-agent systems (MAS) based on large language models (LLMs) have emerged as a powerful solution for dealing with complex problems across diverse domains.<n>Existing approaches are fundamentally constrained by their reliance on a template graph modification paradigm with a predefined set of agents and hard-coded interaction structures.<n>We propose ARG-Designer, a novel autoregressive model that operationalizes this paradigm by constructing the collaboration graph from scratch.
• Score: 72.44384066166147
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multi-agent systems (MAS) based on large language models (LLMs) have emerged as a powerful solution for dealing with complex problems across diverse domains. The effectiveness of MAS is critically dependent on its collaboration topology, which has become a focal point for automated design research. However, existing approaches are fundamentally constrained by their reliance on a template graph modification paradigm with a predefined set of agents and hard-coded interaction structures, significantly limiting their adaptability to task-specific requirements. To address these limitations, we reframe MAS design as a conditional autoregressive graph generation task, where both the system composition and structure are designed jointly. We propose ARG-Designer, a novel autoregressive model that operationalizes this paradigm by constructing the collaboration graph from scratch. Conditioned on a natural language task query, ARG-Designer sequentially and dynamically determines the required number of agents, selects their appropriate roles from an extensible pool, and establishes the optimal communication links between them. This generative approach creates a customized topology in a flexible and extensible manner, precisely tailored to the unique demands of different tasks. Extensive experiments across six diverse benchmarks demonstrate that ARG-Designer not only achieves state-of-the-art performance but also enjoys significantly greater token efficiency and enhanced extensibility. The source code of ARG-Designer is available at https://github.com/Shiy-Li/ARG-Designer.

Related papers

• Structural Similarity-Inspired Unfolding for Lightweight Image Super-Resolution [88.20464308588889]
  We propose a Structural Similarity-Inspired Unfolding (SSIU) method for efficient image SR.<n>This method is designed through unfolding an SR optimization function constrained by structural similarity.<n>Our model outperforms current state-of-the-art models, boasting lower parameter counts and reduced memory consumption.
  arXiv  Detail & Related papers  (2025-06-13T14:29:40Z)
• Comparative Analysis of AI Agent Architectures for Entity Relationship Classification [1.6887793771613606]
  In this study, we conduct a comparative analysis of three distinct AI agent architectures to perform relation classification.<n>The agentic architectures explored include (1) reflective self-evaluation, (2) hierarchical task decomposition, and (3) a novel multi-agent dynamic example generation mechanism.<n>Our experiments demonstrate that multi-agent coordination consistently outperforms standard few-shot prompting.
  arXiv  Detail & Related papers  (2025-06-03T04:19:47Z)
• CreatiDesign: A Unified Multi-Conditional Diffusion Transformer for Creative Graphic Design [69.83433430133302]
  CreatiDesign is a systematic solution for automated graphic design covering both model architecture and dataset construction.<n>First, we design a unified multi-condition driven architecture that enables flexible and precise integration of heterogeneous design elements.<n> Furthermore, to ensure that each condition precisely controls its designated image region, we propose a multimodal attention mask mechanism.
  arXiv  Detail & Related papers  (2025-05-25T12:14:23Z)
• MAS-ZERO: Designing Multi-Agent Systems with Zero Supervision [76.42361936804313]
  We introduce MAS-ZERO, the first self-evolved, inference-time framework for automatic MAS design.<n> MAS-ZERO employs meta-level design to iteratively generate, evaluate, and refine MAS configurations tailored to each problem instance.
  arXiv  Detail & Related papers  (2025-05-21T00:56:09Z)
• HM-RAG: Hierarchical Multi-Agent Multimodal Retrieval Augmented Generation [11.53083922927901]
  HM-RAG is a novel Hierarchical Multi-agent Multimodal RAG framework.<n>It pioneers collaborative intelligence for dynamic knowledge synthesis across structured, unstructured, and graph-based data.
  arXiv  Detail & Related papers  (2025-04-13T06:55:33Z)
• Instruction-Guided Autoregressive Neural Network Parameter Generation [49.800239140036496]
  We propose IGPG, an autoregressive framework that unifies parameter synthesis across diverse tasks and architectures.<n>By autoregressively generating neural network weights' tokens, IGPG ensures inter-layer coherence and enables efficient adaptation across models and datasets.<n>Experiments on multiple datasets demonstrate that IGPG consolidates diverse pretrained models into a single, flexible generative framework.
  arXiv  Detail & Related papers  (2025-04-02T05:50:19Z)
• A representational framework for learning and encoding structurally enriched trajectories in complex agent environments [1.904851064759821]
  The ability of artificial intelligence agents to make optimal decisions and generalise them to different domains and tasks is compromised in complex scenarios.<n>One way to address this issue has focused on learning efficient representations of the world and on how the actions of agents affect them, such as disentangled representations that exploit symmetries.<n>We propose to enrich the agent's ontology and extend the traditionalisation of trajectories to provide a more nuanced view of task execution.
  arXiv  Detail & Related papers  (2025-03-17T14:04:27Z)
• G-Designer: Architecting Multi-agent Communication Topologies via Graph Neural Networks [36.98849144472012]
  We introduce G-Designer, an adaptive, efficient, and robust solution for multi-agent deployment.<n>G-Designer dynamically designs task-aware, customized communication topologies.
  arXiv  Detail & Related papers  (2024-10-15T17:01:21Z)
• PosterLLaVa: Constructing a Unified Multi-modal Layout Generator with LLM [58.67882997399021]
  Our research introduces a unified framework for automated graphic layout generation.<n>Our data-driven method employs structured text (JSON format) and visual instruction tuning to generate layouts.<n>We develop an automated text-to-poster system that generates editable posters based on users' design intentions.
  arXiv  Detail & Related papers  (2024-06-05T03:05:52Z)
• TDAG: A Multi-Agent Framework based on Dynamic Task Decomposition and Agent Generation [41.21899915378596]
  We propose a multi-agent framework based on dynamic Task Decomposition and Agent Generation (TDAG)<n>This framework dynamically decomposes complex tasks into smaller subtasks and assigns each to a specifically generated subagent.<n>ItineraryBench is designed to assess agents' abilities in memory, planning, and tool usage across tasks of varying complexity.
  arXiv  Detail & Related papers  (2024-02-15T18:27:37Z)
• Multi-Agent Reinforcement Learning for Microprocessor Design Space Exploration [71.95914457415624]
  Microprocessor architects are increasingly resorting to domain-specific customization in the quest for high-performance and energy-efficiency. We propose an alternative formulation that leverages Multi-Agent RL (MARL) to tackle this problem. Our evaluation shows that the MARL formulation consistently outperforms single-agent RL baselines.
  arXiv  Detail & Related papers  (2022-11-29T17:10:24Z)

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.