OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation

• URL: http://arxiv.org/abs/2505.23885v2
• Date: Wed, 11 Jun 2025 01:42:53 GMT
• Title: OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation
• Authors: Mengkang Hu, Yuhang Zhou, Wendong Fan, Yuzhou Nie, Bowei Xia, Tao Sun, Ziyu Ye, Zhaoxuan Jin, Yingru Li, Qiguang Chen, Zeyu Zhang, Yifeng Wang, Qianshuo Ye, Bernard Ghanem, Ping Luo, Guohao Li,
• Abstract summary: We introduce Workforce, a hierarchical multi-agent framework that decouples strategic planning from specialized execution.<n>During inference, Workforce seamlessly adapts to new domains by adding or modifying worker agents.<n>For training, we introduce optimized Workforce Learning (OWL), which improves generalization across domains.
• Score: 65.15955645757705
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Model (LLM)-based multi-agent systems show promise for automating real-world tasks but struggle to transfer across domains due to their domain-specific nature. Current approaches face two critical shortcomings: they require complete architectural redesign and full retraining of all components when applied to new domains. We introduce Workforce, a hierarchical multi-agent framework that decouples strategic planning from specialized execution through a modular architecture comprising: (i) a domain-agnostic Planner for task decomposition, (ii) a Coordinator for subtask management, and (iii) specialized Workers with domain-specific tool-calling capabilities. This decoupling enables cross-domain transferability during both inference and training phases: During inference, Workforce seamlessly adapts to new domains by adding or modifying worker agents; For training, we introduce Optimized Workforce Learning (OWL), which improves generalization across domains by optimizing a domain-agnostic planner with reinforcement learning from real-world feedback. To validate our approach, we evaluate Workforce on the GAIA benchmark, covering various realistic, multi-domain agentic tasks. Experimental results demonstrate Workforce achieves open-source state-of-the-art performance (69.70%), outperforming commercial systems like OpenAI's Deep Research by 2.34%. More notably, our OWL-trained 32B model achieves 52.73% accuracy (+16.37%) and demonstrates performance comparable to GPT-4o on challenging tasks. To summarize, by enabling scalable generalization and modular domain transfer, our work establishes a foundation for the next generation of general-purpose AI assistants.

Related papers

• 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)
• Omni-Thinker: Scaling Cross-Domain Generalization in LLMs via Multi-Task RL with Hybrid Rewards [50.21528417884747]
  We introduce Omni-Thinker, a unified reinforcement learning framework that enhances large language models (LLMs) performance across diverse tasks.<n>Our approach enables consistent optimization across task types and scales RL-based training to subjective domains.<n> Experimental results across four domains reveal that curriculum learning improves performance by 5.2% over joint training and 9.1% over model merging.
  arXiv  Detail & Related papers  (2025-07-20T01:50:16Z)
• Towards Unified Modeling in Federated Multi-Task Learning via Subspace Decoupling [23.642760378344335]
  Federated Multi-Task Learning (FMTL) enables multiple clients performing heterogeneous tasks without exchanging their local data.<n>Most existing FMTL methods focus on building personalized models for each client and unable to support the aggregation of multiple heterogeneous tasks into a unified model.<n>We propose FedDEA, an update-structure-aware aggregation method specifically designed for multi-task model integration.
  arXiv  Detail & Related papers  (2025-05-30T03:53:21Z)
• Multi-Agent Reinforcement Learning with Long-Term Performance Objectives for Service Workforce Optimization [2.865067924658368]
  Our aim is to create a simulator that models a unified workforce optimization problem.<n> Specifically, we designed a modular simulator to support the development of reinforcement learning methods.<n>The simulator provides parameterizations to help explore dynamic scenarios with varying levels of ablationity and non-stationarity.
  arXiv  Detail & Related papers  (2025-03-03T00:16:47Z)
• More Than Catastrophic Forgetting: Integrating General Capabilities For Domain-Specific LLMs [40.54076184225558]
  The performance on general tasks decreases after Large Language Models (LLMs) are fine-tuned on domain-specific tasks, known as Catastrophic Forgetting (CF) This paper presents a challenge for real application of domain-specific LLMs beyond CF, called General Capabilities Integration (GCI) The objective of GCI is not merely to retain previously acquired general capabilities alongside new domain knowledge, but to harmonize and utilize both sets of skills in a cohesive manner to enhance performance on domain-specific tasks.
  arXiv  Detail & Related papers  (2024-05-28T05:00:12Z)
• 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)
• Human-Timescale Adaptation in an Open-Ended Task Space [56.55530165036327]
  We show that training an RL agent at scale leads to a general in-context learning algorithm that can adapt to open-ended novel embodied 3D problems as quickly as humans. Our results lay the foundation for increasingly general and adaptive RL agents that perform well across ever-larger open-ended domains.
  arXiv  Detail & Related papers  (2023-01-18T15:39:21Z)
• MultiMatch: Multi-task Learning for Semi-supervised Domain Generalization [55.06956781674986]
  We resort to solving the semi-supervised domain generalization task, where there are a few label information in each source domain. We propose MultiMatch, extending FixMatch to the multi-task learning framework, producing the high-quality pseudo-label for SSDG. A series of experiments validate the effectiveness of the proposed method, and it outperforms the existing semi-supervised methods and the SSDG method on several benchmark DG datasets.
  arXiv  Detail & Related papers  (2022-08-11T14:44:33Z)
• Unsupervised Domain Generalization for Person Re-identification: A Domain-specific Adaptive Framework [50.88463458896428]
  Domain generalization (DG) has attracted much attention in person re-identification (ReID) recently. Existing methods usually need the source domains to be labeled, which could be a significant burden for practical ReID tasks. We propose a simple and efficient domain-specific adaptive framework, and realize it with an adaptive normalization module.
  arXiv  Detail & Related papers  (2021-11-30T02:35:51Z)
• TAL: Two-stream Adaptive Learning for Generalizable Person Re-identification [115.31432027711202]
  We argue that both domain-specific and domain-invariant features are crucial for improving the generalization ability of re-id models. We name two-stream adaptive learning (TAL) to simultaneously model these two kinds of information. Our framework can be applied to both single-source and multi-source domain generalization tasks.
  arXiv  Detail & Related papers  (2021-11-29T01:27:42Z)

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.