---
title: "NARU：面向日本超长视频的叙事演进与文化细微差别理解基准"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2608.13210v1"
permalink: "https://feed.astralor.com/items/itm_b6f24bc3310e1d79a53b7653"
published: "2026-08-13T13:13:43.000Z"
score: 78
channel: "ai"
tags: ["NARU","多模态大语言模型","文化推理","长视频理解"]
---

# NARU：面向日本超长视频的叙事演进与文化细微差别理解基准

## AI 点评

> 作为情报编辑，我认为NARU的关键价值在于把文化背景从'噪声'变成了评测维度。对做多模态研究或想进入日本市场的团队，这份基准提供了难得的测试场。尤其值得关注的是68名母语者的校验设计，大幅提升了数据可信度。

## 速览

机器学习社区长期缺乏评估长视频叙事跟踪与跨文化隐含意义理解的任务。NARU基准填补这一空白，包含155个视频、146.8小时时长，标注1481个问题，覆盖四个叙事维度与五个文化维度。通过层级记忆管线生成注释并经过68名日语母语者双重校验。在8种模型配置上评测发现，多模态大语言模型在长程叙事整合和文化推理上仍存在显著局限。该基准为提升高语境长视频的可靠理解提供了系统测试场。

## 关键点

- 首次构建面向日本极端长视频的叙事演进与文化细微差别理解基准，包含155个视频、146.8小时与1481个问题。
- 提出层级记忆标注流程，将原始视频转化为事件、叙事与文化标注，并经过68名母语者双重校验。
- 评测8种模型配置，发现长程叙事整合与文化推理仍是多模态大语言模型的突出短板。
- 为高语境非英语长视频理解提供系统测试场，推动MLLM向可靠长视频推理演进。

## 全文

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.

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引用前请核对原文;本文由星际流动整理:https://feed.astralor.com/items/itm_b6f24bc3310e1d79a53b7653