Diffusion video models produce striking clips, but they cannot reliably render an exact formula, a correct chart, a rule-consistent game or narration synchronized to on-screen events. A different rout
VibeVideoBench evaluates multimodal agents that generate videos via code, finding that top systems meet core requirements on most tasks but frequently fail due to poor time management.
Diffusion video models produce striking clips, but they cannot reliably render an exact formula, a correct chart, a rule-consistent game or narration synchronized to on-screen events. A different route is emerging: multimodal agents that write code to render every frame and synthesize every sound. We introduce VibeVideoBench, a benchmark for evaluating agents on code-driven video generation. Its 239 self-contained tasks in English and Chinese span 12 categories, from data stories and music-driven pieces to classroom lessons and paper explainers; each asks an agent, running in its native harness inside a sandbox with no image or video generation model, to deliver a finished MP4 with sound…
Summary and abstract excerpt via Hugging Face Papers ↗ · arXiv.
VibeVideoBench: Evaluating Multimodal Agents on Code-Driven Video Generation was first seen on Hugging Face · Daily papers on Oct 28, 2026. Since then it has appeared in 23 checks on 3 different days. Its best position was #3 of 30, reached on Oct 29, 17:28 UTC. It dropped off the list after Oct 30, 03:28 UTC.
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Not at the moment. It was last on the list on Oct 30, 03:28 UTC, after 3 days of trending.
#3 on Hugging Face · Daily papers, on Oct 29, 17:28 UTC. Trendoday checks this list every 120 minutes.
It was first seen on Oct 28, 2026 and has been on the list on 3 days (23 checks) since.