Researchers have published EntityBench, an arXiv benchmark designed to evaluate entity consistency in long-range, multi-shot video generation — a capability gap they say existing benchmarks largely leave unmeasured. The benchmark targets a specific failure mode in current video generation models: the tendency to drift in character, object, or scene identity across extended sequences and scene transitions. EntityBench provides standardized metrics to isolate and quantify this as a distinct evaluation dimension, separate from broader video quality assessments. According to the paper, no widely adopted benchmark currently addresses multi-shot entity consistency in a structured way, leaving engineering teams without a reliable signal for whether model iterations are actually improving on this axis. The work is framed as an evaluation tool rather than a training method or architecture proposal. Teams building or stress-testing video generation pipelines can use EntityBench to establish baselines and track targeted improvements across model versions without conflating entity consistency with other quality dimensions.