HAWK: A Hierarchical Workflow Framework for Multi-Agent Collaboration

• URL: http://arxiv.org/abs/2507.04067v1
• Date: Sat, 05 Jul 2025 15:03:53 GMT
• Title: HAWK: A Hierarchical Workflow Framework for Multi-Agent Collaboration
• Authors: Yuyang Cheng, Yumiao Xu, Chaojia Yu, Yong Zhao,
• Abstract summary: Multi-agent systems face persistent challenges in cross-platform interoperability, dynamic task scheduling, and efficient resource sharing.<n>We propose Hierarchical Agent (Hawk), a modular framework comprising five layers-User, Operator, Agent, Resource-and supported by sixteen standardized interfaces.<n>Hawk delivers an end-to-end pipeline covering task parsing, workflow orchestration, intelligent scheduling, resource invocation, and data synchronization.
• Score: 3.2588674134593942
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Contemporary multi-agent systems encounter persistent challenges in cross-platform interoperability, dynamic task scheduling, and efficient resource sharing. Agents with heterogeneous implementations often lack standardized interfaces; collaboration frameworks remain brittle and hard to extend; scheduling policies are static; and inter-agent state synchronization is insufficient. We propose Hierarchical Agent Workflow (HAWK), a modular framework comprising five layers-User, Workflow, Operator, Agent, and Resource-and supported by sixteen standardized interfaces. HAWK delivers an end-to-end pipeline covering task parsing, workflow orchestration, intelligent scheduling, resource invocation, and data synchronization. At its core lies an adaptive scheduling and optimization module in the Workflow Layer, which harnesses real-time feedback and dynamic strategy adjustment to maximize utilization. The Resource Layer provides a unified abstraction over heterogeneous data sources, large models, physical devices, and third-party services&tools, simplifying cross-domain information retrieval. We demonstrate HAWK's scalability and effectiveness via CreAgentive, a multi-agent novel-generation prototype, which achieves marked gains in throughput, lowers invocation complexity, and improves system controllability. We also show how hybrid deployments of large language models integrate seamlessly within HAWK, highlighting its flexibility. Finally, we outline future research avenues-hallucination mitigation, real-time performance tuning, and enhanced cross-domain adaptability-and survey prospective applications in healthcare, government, finance, and education.

Related papers

• FlowSteer: Interactive Agentic Workflow Orchestration via End-to-End Reinforcement Learning [49.369614288007334]
  FlowSteer is an end-to-end reinforcement learning framework that takes a lightweight policy model as the agent and an executable canvas environment.<n>We show that FlowSteer significantly outperforms baselines across various tasks.
  arXiv  Detail & Related papers  (2026-02-02T05:30:42Z)
• Matrix: Peer-to-Peer Multi-Agent Synthetic Data Generation Framework [32.3041485160475]
  We present textbf Matrix, a decentralized framework for multi-agent synthesis.<n> Matrix represents both control and data flow as serialized messages pass through distributed queues.<n>We evaluate Matrix across diverse synthesis scenarios, such as multi-agent collaborative dialogue, web-based reasoning data extraction, and tool-use trajectory generation in customer service environments.
  arXiv  Detail & Related papers  (2025-11-26T18:59:28Z)
• Modular Task Decomposition and Dynamic Collaboration in Multi-Agent Systems Driven by Large Language Models [3.4219049032524804]
  This paper addresses the limitations of a single agent in task decomposition and collaboration during complex task execution.<n>It proposes a multi-agent architecture for modular task decomposition and dynamic collaboration based on large language models.
  arXiv  Detail & Related papers  (2025-11-03T02:00:06Z)
• Dynamic Generation of Multi-LLM Agents Communication Topologies with Graph Diffusion Models [99.85131798240808]
  We introduce a novel generative framework called textitGuided Topology Diffusion (GTD)<n>Inspired by conditional discrete graph diffusion models, GTD formulates topology synthesis as an iterative construction process.<n>At each step, the generation is steered by a lightweight proxy model that predicts multi-objective rewards.<n>Experiments show that GTD can generate highly task-adaptive, sparse, and efficient communication topologies.
  arXiv  Detail & Related papers  (2025-10-09T05:28:28Z)
• CollaPipe: Adaptive Segment-Optimized Pipeline Parallelism for Collaborative LLM Training in Heterogeneous Edge Networks [57.95170323315603]
  We introduce CollaPipe, a distributed learning framework that integrates collaborative pipeline parallelism with federated aggregation to support self-evolving networks.<n>In CollaPipe, the encoder part is adaptively partitioned into variable-sized segments and deployed across mobile devices for pipeline-parallel training, while the decoder is deployed on edge servers to handle generative tasks.<n>To enhance training efficiency, we formulate a joint optimization problem that adaptively allocates model segments, micro-batches, bandwidth, and transmission power.
  arXiv  Detail & Related papers  (2025-09-24T07:54:01Z)
• Odoo-based Subcontract Inter-site Access Control Mechanism for Construction Projects [0.0]
  In era of Construction 4.0, industry is embracing a new paradigm of labor elasticity, driven by smart and flexible outsourcing and subcontracting strategies.<n>Increased reliance on specialized subcontractors enables companies to scale labor dynamically based on project demands.<n>This adaptable workforce model presents challenges in managing hierarchical integration and coordinating inter-site collaboration.
  arXiv  Detail & Related papers  (2025-09-05T14:38:19Z)
• Assemble Your Crew: Automatic Multi-agent Communication Topology Design via Autoregressive Graph Generation [72.44384066166147]
  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.
  arXiv  Detail & Related papers  (2025-07-24T09:17:41Z)
• Agent WARPP: Workflow Adherence via Runtime Parallel Personalization [0.0]
  Large language models (LLMs) are increasingly applied in task-oriented dialogue (TOD) systems.<n>We present Adherence via Parallel Personalization, or WARPP, a training-free, modular framework that combines multi-agent runtime with orchestration.<n>By dynamically pruning conditional branches based on user attributes, the framework reduces reasoning overhead and narrows tool selection at runtime.
  arXiv  Detail & Related papers  (2025-07-23T23:27:49Z)
• Parallelism Meets Adaptiveness: Scalable Documents Understanding in Multi-Agent LLM Systems [0.8437187555622164]
  Large language model (LLM) agents have shown increasing promise for collaborative task completion.<n>Existing multi-agent frameworks often rely on static, fixed roles, and limited inter-agent communication.<n>This paper proposes a coordination framework that enables adaptiveness through three core mechanisms.
  arXiv  Detail & Related papers  (2025-07-22T22:42:51Z)
• HedraRAG: Coordinating LLM Generation and Database Retrieval in Heterogeneous RAG Serving [10.130938079844121]
  HedraRAG is a runtime system built on a graph-based abstraction that exposes optimization opportunities across stage-level parallelism, intra-request similarity, and inter-request skewness.<n>The resulting execution plans are mapped onto hybrid CPU-GPU pipelines to improve resource utilization and reduce latency.
  arXiv  Detail & Related papers  (2025-07-12T04:42:43Z)
• Gradientsys: A Multi-Agent LLM Scheduler with ReAct Orchestration [4.66888457790348]
  We present Gradientsys, a next-generation multi-agent scheduling framework.<n>It coordinates diverse specialized AI agents using a typed Model-Context Protocol (MCP) and a ReAct-based dynamic planning loop.<n>Experiments on the GAIA general-assistant benchmark show that Gradientsys achieves higher task success rates with reduced latency and lower API costs.
  arXiv  Detail & Related papers  (2025-07-09T03:40:56Z)
• AnyMAC: Cascading Flexible Multi-Agent Collaboration via Next-Agent Prediction [70.60422261117816]
  We propose a new framework that rethinks multi-agent coordination through a sequential structure rather than a graph structure.<n>Our method focuses on two key directions: (1) Next-Agent Prediction, which selects the most suitable agent role at each step, and (2) Next-Context Selection, which enables each agent to selectively access relevant information from any previous step.
  arXiv  Detail & Related papers  (2025-06-21T18:34:43Z)
• Multi-Agent Collaboration via Evolving Orchestration [55.574417128944226]
  Large language models (LLMs) have achieved remarkable results across diverse downstream tasks, but their monolithic nature restricts scalability and efficiency in complex problem-solving.<n>We propose a puppeteer-style paradigm for LLM-based multi-agent collaboration, where a centralized orchestrator ("puppeteer") dynamically directs agents ("puppets") in response to evolving task states.<n> Experiments on closed- and open-domain scenarios show that this method achieves superior performance with reduced computational costs.
  arXiv  Detail & Related papers  (2025-05-26T07:02:17Z)
• Dynamic Allocation Hypernetwork with Adaptive Model Recalibration for Federated Continual Learning [49.508844889242425]
  We propose a novel server-side FCL pattern in medical domain, Dynamic Allocation Hypernetwork with adaptive model recalibration (FedDAH)<n>FedDAH is designed to facilitate collaborative learning under the distinct and dynamic task streams across clients.<n>For the biased optimization, we introduce a novel adaptive model recalibration (AMR) to incorporate the candidate changes of historical models into current server updates.
  arXiv  Detail & Related papers  (2025-03-25T00:17:47Z)
• Dynamic Allocation Hypernetwork with Adaptive Model Recalibration for FCL [49.508844889242425]
  We propose a novel server-side FCL pattern in medical domain, Dynamic Allocation Hypernetwork with adaptive model recalibration (textbfFedDAH)<n>For the biased optimization, we introduce a novel adaptive model recalibration (AMR) to incorporate the candidate changes of historical models into current server updates.<n>Experiments on the AMOS dataset demonstrate the superiority of our FedDAH to other FCL methods on sites with different task streams.
  arXiv  Detail & Related papers  (2025-03-23T13:12:56Z)
• 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)
• Benchmarking Agentic Workflow Generation [80.74757493266057]
  We introduce WorfBench, a unified workflow generation benchmark with multi-faceted scenarios and intricate graph workflow structures.<n>We also present WorfEval, a systemic evaluation protocol utilizing subsequence and subgraph matching algorithms.<n>We observe that the generated can enhance downstream tasks, enabling them to achieve superior performance with less time during inference.
  arXiv  Detail & Related papers  (2024-10-10T12:41:19Z)
• ComfyBench: Benchmarking LLM-based Agents in ComfyUI for Autonomously Designing Collaborative AI Systems [80.69865295743149]
  This work attempts to study using LLM-based agents to design collaborative AI systems autonomously.<n>Based on ComfyBench, we develop ComfyAgent, a framework that empowers agents to autonomously design collaborative AI systems by generating.<n>While ComfyAgent achieves a comparable resolve rate to o1-preview and significantly surpasses other agents on ComfyBench, ComfyAgent has resolved only 15% of creative tasks.
  arXiv  Detail & Related papers  (2024-09-02T17:44:10Z)
• A Bayesian Framework of Deep Reinforcement Learning for Joint O-RAN/MEC Orchestration [12.914011030970814]
  Multi-access Edge Computing (MEC) can be implemented together with Open Radio Access Network (O-RAN) over commodity platforms to offer low-cost deployment. In this paper, a joint O-RAN/MEC orchestration using a Bayesian deep reinforcement learning (RL)-based framework is proposed.
  arXiv  Detail & Related papers  (2023-12-26T18:04:49Z)

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.