NARU: A Benchmark for Narrative Evolution and Cultural Nuance Understanding in Japanese Extreme Long Video
Score
78首次将叙事演进与文化细微差别在非英语超长视频中联合评测，填补现有基准空白，信息增量显著。
采用层级记忆标注与68名母语者双重校验，数据质量高，问题生成去除捷径干扰。
为长视频理解模型提供系统测试场，可直接用于多模态模型评测和改进，但适用面受限于日语语料。
长视频理解与高语境文化推理是当前多模态研究热点，该工作与现实需求高度同步。
来源于arXiv预印本，注释流程详实且经过多轮人工验证，可靠性较强。
Base 86.5 × T2 Source weight ×0.9 = 78
Curation line 70Selected
Why selected
The paper delivers a novel, human-verified benchmark targeting a valuable gap, yet the limited evaluation across eight model configurations leaves room for broader generalization.
AI commentary
「As an intelligence editor, I see NARU's key value in turning cultural context from noise into a core evaluation dimension. For multimodal researchers or teams targeting Japanese markets, this benchmark offers a rare testbed. The two-stage native-speaker verification involving 68 annotators is especially noteworthy for enhancing data credibility.」
Quick overview
The machine learning community has long lacked tasks that jointly evaluate narrative tracking and implicit cultural understanding in long-form video. NARU fills this gap with 155 videos totaling 146.8 hours and 1,481 questions spanning four narrative and five cultural dimensions. A hierarchical memory-based annotation pipeline converts raw video into structured annotations, with two native-speaker verification stages involving 68 annotators. Evaluations across eight model configurations reveal significant limitations in both long-range narrative integration and culturally grounded reasoning. The benchmark provides a systematic testing ground for making multimodal LLMs reliably interpret high-context long-form video.
- Introduces NARU, the first benchmark for narrative evolution and cultural nuance understanding in Japanese extreme long video, with 155 videos, 146.8 hours, and 1,481 questions.
- Proposes a hierarchical memory-based annotation pipeline that turns raw video into event, narrative, and cultural annotations, verified by 68 native speakers.
- Evaluates eight model configurations, showing long-range narrative integration and culturally grounded reasoning remain major bottlenecks for MLLMs.
- Offers a systematic testbed for high-context non-English long video understanding, guiding progress toward reliable long-video reasoning.
Full text
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