Sandboxed Coding Agents are Competitive Omni-modal Task Solvers
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, frames, and other modality signals, thereby converting omnimodal tasks into retrieval and information-processing problems rather than ingesting ent...
| Authors | Dongping Chen, Xuanao Huang, Zhihan Hu et al. |
|---|---|
| Primary category | cs.CL |
| Published | 30 May 2026 |
| Last updated at source | 30 May 2026 |
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