arXiv:2602.22897v3 Announce Type: replace-cross 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 v
The proliferation of advanced LLMs and multimodal research is naturally leading to efforts to integrate diverse sensory inputs and reasoning capabilities for more general AI agents.
This development addresses a critical limitation of current AI, moving towards agents that can understand and interact with the world through multiple senses and complex reasoning, similar to human intelligence.
The focus of AI agent development expands beyond primarily language-based or bi-modal systems to genuinely omni-modal agents capable of deeper, multi-turn, and tool-augmented interactions.
- · AI research labs
- · Omni-modal AI platform providers
- · AI agent developers
- · Robotics
- · Developers solely focused on uni-modal or bi-modal AI
- · Companies unable to integrate diverse data streams
New benchmarks and architectural patterns emerge for truly omni-modal AI agents.
The development accelerates towards more autonomous and capable general AI assistants and robotic systems.
The boundaries between AI, robotics, and human-computer interaction blur as agents become more adept at understanding physical and digital environments simultaneously.
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Read at arXiv cs.CL