OmniGAIA: Towards Native Omni-Modal AI Agents
arXiv:2602.22897v1 Announce Type: new 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 sy
arXiv:2602.22897v1 Announce Type: new 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.
Executive Summary
The article proposes OmniGAIA, a comprehensive benchmark to evaluate omni-modal AI agents, and OmniAtlas, a native omni-modal foundation agent. OmniGAIA assesses agents' ability to reason and execute tasks across multiple modalities, including video, audio, and image. OmniAtlas is trained on synthesized data using a hindsight-guided tree exploration strategy and fine-grained error correction. The work aims to develop next-generation AI assistants for real-world scenarios. The introduction of OmniGAIA and OmniAtlas is a significant step towards creating more robust and versatile AI agents.
Key Points
- ▸ OmniGAIA is a novel benchmark for evaluating omni-modal AI agents.
- ▸ OmniAtlas is a native omni-modal foundation agent under the tool-integrated reasoning paradigm.
- ▸ The work synthesizes complex, multi-hop queries derived from real-world data for evaluating cross-modal reasoning and tool integration.
Merits
Strength
The introduction of OmniGAIA and OmniAtlas addresses the current limitations of multi-modal LLMs and provides a unified cognitive framework for general AI assistants.
Strength
The use of hindsight-guided tree exploration strategy and OmniDPO for fine-grained error correction enhances the tool-use capabilities of existing open-source models.
Strength
The work has the potential to revolutionize the development of AI assistants for real-world scenarios by leveraging omni-modal perception and tool usage.
Demerits
Limitation
The proposed benchmark and agent may require significant computational resources and data for training and evaluation.
Limitation
The current version of OmniAtlas may not be applicable to all real-world scenarios due to the complexity of the tasks and the need for fine-grained error correction.
Expert Commentary
The article represents a significant contribution to the field of AI research, as it addresses the current limitations of multi-modal LLMs and provides a unified cognitive framework for general AI assistants. The introduction of OmniGAIA and OmniAtlas is a crucial step towards creating more robust and versatile AI agents. However, the proposed benchmark and agent may require significant computational resources and data for training and evaluation, and the current version of OmniAtlas may not be applicable to all real-world scenarios due to the complexity of the tasks and the need for fine-grained error correction. Overall, the work has the potential to revolutionize the development of AI assistants for real-world scenarios by leveraging omni-modal perception and tool usage.
Recommendations
- ✓ Future research should focus on developing more efficient and scalable methods for training and evaluating OmniGAIA and OmniAtlas.
- ✓ The developers of OmniGAIA and OmniAtlas should prioritize the explainability and transparency of the agents to ensure their adoption in critical applications.
