VCIFBench: Evaluating Complex Instruction Following for Video Understanding
Multimodal large language models have made rapid progress in video understanding, yet existing benchmarks largely rely on simple prompts and provide limited evidence about whether models can satisfy explicit output constraints. We introduce VCIFBench, a benchmark for evaluating complex instruction following in video understanding. VCIFBench constructs constraint-rich instructions from both benchmark-adapted and directly video-grounded prompts, covering content, format, style, and structure requirements, and evaluates model outputs with a hybrid verification pipeline. The benchmark contains 306 satisfiable test instructions, 540 DPO training instances, and a 100-item diagnostic set for eva...
| Authors | Huangchen Xu, Yuan Wu, Yi Chang |
|---|---|
| Primary category | cs.CL |
| Published | 3 June 2026 |
| Last updated at source | 23 August 2026 |
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