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NARU: A Benchmark for NARrative Evolution and Cultural Nuance Understanding in Japanese Extreme Long Video — arXiv:2608.13210

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.13210
License (as archived): CC0-1.0
Archived: 2026-10-19T04:18:23.623Z
SHA-256 of the archived text: e23aee96af1e1a3f4de0600d6dba81fcb8e098e03b9fca32f247baa723142d4a
Stable URL of this page: https://metraipolis.com/documento/dc_7bc533253cbb

How to cite

NARU: A Benchmark for NARrative Evolution and Cultural Nuance Understanding in Japanese Extreme Long Video — arXiv:2608.13210 — arXiv (en), https://arxiv.org/abs/2608.13210 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_7bc533253cbb (Metraipolis archive dc_7bc533253cbb, sha256 e23aee96af1e1a3f4de0600d6dba81fcb8e098e03b9fca32f247baa723142d4a)

BibTeX

@misc{dc7bc533253cbb,
  title = {NARU: A Benchmark for NARrative Evolution and Cultural Nuance Understanding in Japanese Extreme Long Video — arXiv:2608.13210},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.13210},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_7bc533253cbb; sha256 e23aee96af1e1a3f4de0600d6dba81fcb8e098e03b9fca32f247baa723142d4a},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.13210. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Long-form video understanding encompasses tasks that go beyond retrieving isolated events, including tracking an evolving narrative and interpreting social meaning that may remain implicit. However, existing benchmarks rarely evaluate these capabilities jointly, particularly in high-context, non-English media. To address this gap, we introduce NARU, a benchmark designed to evaluate Narrative evolution and Reasoning on cultural Understanding in Japanese long-form video. NARU consists of 1,481 questions grounded in 155 videos totaling 146.8 hours, spanning four narrative and five cultural dimensions. To construct the benchmark at this scale, we propose a hierarchical memory-based annotation pipeline that transforms raw video into structured event, narrative, and cultural annotations, then generates questions via task-oriented synthesis and iterative shortcut removal. The construction process includes two native-speaker verification stages involving 68 annotators. Evaluations across eight model configurations reveal substantial limitations in both long-range narrative integration and culturally grounded reasoning. By exposing these persistent gaps, NARU offers a systematic testing ground for developing MLLMs capable of reliably interpreting long-form, high-context video.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-14 in cs.AI).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV) ; Artificial Intelligence (cs.AI); Multimedia (cs.MM).
Pagina del lavoro: https://arxiv.org/abs/2608.13210 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-19T04:13:06.744Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-08-14?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).