OmniGAIA: Towards Native Omni-Modal AI Agents
#OmniGAIA #omni-modal AI #multi-modal LLMs #benchmark #OmniAtlas #artificial intelligence #machine learning #tool-integrated reasoning
📌 Key Takeaways
- Researchers introduced OmniGAIA benchmark for evaluating omni-modal AI agents
- Current multi-modal LLMs are limited to bi-modal interactions
- OmniGAIA uses omni-modal event graph approach for complex multi-hop queries
- OmniAtlas foundation agent enhances tool-use capabilities in existing models
📖 Full Retelling
A team of researchers led by Xiaoxi Li and 10 co-authors introduced OmniGAIA, a comprehensive benchmark for evaluating native omni-modal AI agents, in a paper published on the arXiv preprint server on February 26, 2026. The research addresses the critical limitation of current multi-modal large language models (LLMs) that are primarily confined to bi-modal interactions, such as vision-language, without possessing the unified cognitive capabilities needed for general AI assistants. To bridge this gap, the researchers developed OmniGAIA, which evaluates AI agents on tasks requiring deep reasoning and multi-turn tool execution across video, audio, and image modalities. The benchmark was constructed using a novel omni-modal event graph approach that synthesizes complex, multi-hop queries from real-world data, necessitating cross-modal reasoning and external tool integration. Additionally, the team proposed OmniAtlas, a native omni-modal foundation agent under a tool-integrated reasoning paradigm with active omni-modal perception, trained through a hindsight-guided tree exploration strategy and OmniDPO for fine-grained error correction to enhance tool-use capabilities of existing open-source models.
🏷️ Themes
Artificial Intelligence, Multi-modal Systems, Machine Learning
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Original Source
--> Computer Science > Artificial Intelligence arXiv:2602.22897 [Submitted on 26 Feb 2026] Title: OmniGAIA: Towards Native Omni-Modal AI Agents Authors: Xiaoxi Li , Wenxiang Jiao , Jiarui Jin , Shijian Wang , Guanting Dong , Jiajie Jin , Hao Wang , Yinuo Wang , Ji-Rong Wen , Yuan Lu , Zhicheng Dou View a PDF of the paper titled OmniGAIA: Towards Native Omni-Modal AI Agents, by Xiaoxi Li and 10 other authors View PDF Abstract: Human intelligence naturally intertwines omni-modal perception -- spanning vision, audio, and language -- with complex reasoning and tool usage to interact with the world. However, current multi-modal LLMs are primarily confined to bi-modal interactions (e.g., vision-language), lacking the unified cognitive capabilities required for general AI assistants. To bridge this gap, we introduce OmniGAIA, a comprehensive benchmark designed to evaluate omni-modal agents on tasks necessitating deep reasoning and multi-turn tool execution across video, audio, and image modalities. Constructed via a novel omni-modal event graph approach, OmniGAIA synthesizes complex, multi-hop queries derived from real-world data that require cross-modal reasoning and external tool integration. Furthermore, we propose OmniAtlas, a native omni-modal foundation agent under tool-integrated reasoning paradigm with active omni-modal perception. Trained on trajectories synthesized via a hindsight-guided tree exploration strategy and OmniDPO for fine-grained error correction, OmniAtlas effectively enhances the tool-use capabilities of existing open-source models. This work marks a step towards next-generation native omni-modal AI assistants for real-world scenarios. Subjects: Artificial Intelligence (cs.AI) ; Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG cs.MM) Cite as: arXiv:2602.22897 [cs.AI] (or arXiv:2602.22897v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2602.22897 Focus to learn more arXiv-issued DOI...
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