arXiv:2606.00579v1 Announce Type: new Abstract: As multimodal LLMs increasingly target video and audio, it is often assumed that such tasks require native omnimodal models. We show that this is not always the case: coding agents with only text+image access and a sandboxed tool-use interface can match, and in several settings outperform, SOTA native omnimodal models and predefined multimodal agent scaffolds across multiple audio-video benchmarks. Our trajectory analysis suggests that their strength comes from writing code and orchestrating tools to extract relevant evidence from transcripts, fr
The rapid advancement of multimodal large language models and the increasing focus on agentic systems make this research timely, demonstrating new capabilities for existing models.
This research suggests that highly capable generalist AI agents might not require fundamentally new 'omnimodal' architectures, but rather sophisticated orchestration of existing text and image models.
The perceived technical barrier for developing advanced omnimodal agents might be lower than previously assumed, shifting R&D focus from novel architectures to sophisticated tool-use and orchestration.
- · AI agent developers
- · Companies with existing text+image AI models
- · Researchers in AI orchestration and tool-use
- · Developers solely focused on native omnimodal architectures
- · Companies investing heavily in only new multimodal data types
Enterprise workflows currently requiring specialized multimodal models could begin to be automated by sandboxed coding agents.
This could lead to a faster deployment of AI-powered automation across various industries, including those involving video and audio analysis.
The reduced complexity or cost for developing highly capable agents might accelerate the broader adoption and impact of autonomous AI systems on white-collar work.
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