Orchestra-o1: Omnimodal Agent Orchestration
This work proposes Orchestra-o1, an omnimodal agent orchestration framework that supports efficient collaboration across text, image, audio, and video. It introduces modality-aware task decomposition, online sub-agent specialization, and parallel execution, achieving 10.3% accuracy improvement on the OmniGAIA benchmark. The paper also presents DA-GRPO, a reinforcement learning method that trains Orchestra-o1-8B to state-of-the-art performance among open-source omnimodal agents.
[2606.13707] Orchestra-o1: Omnimodal Agent Orchestration
[Submitted on 10 Jun 2026]
Title:Orchestra-o1: Omnimodal Agent Orchestration
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Abstract:The recent success of agent swarms has shifted the paradigm of large language model (LLM)-based agents from single-agent workflows to multi-agent systems, highlighting the importance of agent orchestration for task decomposition and collaboration. However, existing orchestration frameworks are limited to a narrow set of modalities and struggle to generalize to more complex settings where heterogeneous modalities coexist and interact. This limitation becomes particularly pronounced in omnimodal scenarios, where tasks require the unified understanding and coordination of diverse inputs such as text, image, audio, and video. In this work, we propose Orchestra-o1, an omnimodal agent orchestration framework designed to support efficient agent collaboration across multiple modalities. Orchestra-o1 introduces a unified orchestration mechanism that enables modality-aware task decomposition, online sub-agent specialization, and parallel sub-task execution. This scalable design allows agent systems to effectively tackle complex real-world tasks involving heterogeneous information sources, surpassing the second-best approach by 10.3% accuracy on the OmniGAIA benchmark. Furthermore, we introduce decision-aligned group relative policy optimization (DA-GRPO), an efficient agentic reinforcement learning approach for training Orchestra-o1-8B, which also achieves state-of-the-art performance against all existing open-source omnimodal agents.
Subjects:
Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2606.13707 [cs.AI]
(or arXiv:2606.13707v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2606.13707
arXiv-issued DOI via DataCite
Submission history
From: Fan Zhang [view email] [v1] Wed, 10 Jun 2026 04:50:35 UTC (11,610 KB)
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