A Skill-augmented Agentic Framework and Benchmark for Multi-Video Understanding
Multimodal Large Language Models have achieved strong performance in single-video understanding, yet their ability to reason across multiple videos remains limited. Existing approaches typically concatenate multiple videos into a single input and perform direct inference, which introduces training-inference mismatch, information loss from frame compression, and a lack of explicit cross-video coordination. Meanwhile, current multi-video benchmarks primarily emphasize event-level comparison, leaving identity-level matching, fine-grained discrimination, and structured multi-step reasoning underexplored. To address these gaps, we introduce MVX-Bench, a Multi-Video Cross-Dimension Benchmark th...
| Authors | Yue Zhang, Liqiang Jing, Jia Li et al. |
|---|---|
| Primary category | cs.CV |
| Published | 16 March 2026 |
| Last updated at source | 29 September 2026 |
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