# Cultural Moment Benchmark: Evaluating Video Cultural Reasoning and Grounding in Southeast Asia

arXiv: https://arxiv.org/abs/2608.23065 · HTML: https://mlsanity.com/paper/2608.23065/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Multimodal reasoning, one of 17 directions in LLM reasoning.
- Builds on 25 of its 53 references that are in the map.
- First posted on arXiv 24 Aug 2026.

## Placed in

- LLM reasoning / [Multimodal reasoning](https://mlsanity.com/lr/multimodal-reasoning/)

## Builds on (most cited first, 25 of 25)

- [Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities](https://mlsanity.com/paper/2507.06261/) (2025)
- [Qwen3-VL Technical Report](https://mlsanity.com/paper/2511.21631/) (2025)
- [InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency](https://mlsanity.com/paper/2508.18265/) (2025)
- [OpenAI GPT-5 System Card](https://mlsanity.com/paper/2601.03267/) (2025)
- [DeepSeek-V3 Technical Report](https://mlsanity.com/paper/2412.19437/) (2024)
- [CultureVLM: Characterizing and Improving Cultural Understanding of Vision-Language Models for over 100 Countries](https://mlsanity.com/paper/2501.01282/) (2025)
- [SEA-LION: Southeast Asian Languages in One Network](https://mlsanity.com/paper/2504.05747/) (2025)
- [Evaluating Visual and Cultural Interpretation: The K-Viscuit Benchmark with Human-VLM Collaboration](https://mlsanity.com/paper/2406.16469/) (2024)
- [GIMMICK -- Globally Inclusive Multimodal Multitask Cultural Knowledge Benchmarking](https://mlsanity.com/paper/2502.13766/) (2025)
- [Exploiting Semantic Role Contextualized Video Features for Multi-Instance Text-Video Retrieval EPIC-KITCHENS-100 Multi-Instance Retrieval Challenge 2022](https://mlsanity.com/paper/2206.14381/) (2022)
- [MMA-ASIA: A Multilingual and Multimodal Alignment Framework for Culturally-Grounded Evaluation](https://mlsanity.com/paper/2510.08608/) (2025)
- [AVMeme Exam: A Multimodal Multilingual Multicultural Benchmark for LLMs' Contextual and Cultural Knowledge and Thinking](https://mlsanity.com/paper/2601.17645/) (2026)
- [VideoNorms: Benchmarking Cultural Awareness of Video Language Models](https://mlsanity.com/paper/2510.08543/) (2025)
- [ChineseVideoBench: Benchmarking Multi-modal Large Models for Chinese Video Question Answering](https://mlsanity.com/paper/2511.18399/) (2025)
- [Vision Language Models are Confused Tourists](https://mlsanity.com/paper/2511.17004/) (2025)
- [World in a Frame: Understanding Culture Mixing as a New Challenge for Vision-Language Models](https://mlsanity.com/paper/2511.22787/) (2025)
- [Rice-VL: Evaluating Vision-Language Models for Cultural Understanding Across ASEAN Countries](https://mlsanity.com/paper/2512.01419/) (2025)
- [Afri-MCQA: Multimodal Cultural Question Answering for African Languages](https://mlsanity.com/paper/2601.05699/) (2026)
- [Anthropogenic Regional Adaptation in Multimodal Vision-Language Model](https://mlsanity.com/paper/2604.11490/) (2026)
- [VG-TVP: Multimodal Procedural Planning via Visually Grounded Text-Video Prompting](https://mlsanity.com/paper/2412.11621/) (2024)
- [MINERVA-Cultural: A Benchmark for Cultural and Multilingual Long Video Reasoning](https://mlsanity.com/paper/2601.10649/) (2026)
- [ValueGround: Evaluating Culture-Conditioned Visual Value Grounding in MLLMs](https://mlsanity.com/paper/2604.06484/) (2026)
- [When Cultures Move: Measuring and Improving Multicultural Text-to-Video Generation](https://mlsanity.com/paper/2605.16716/) (2026)
- [Jury Duty: Calibration and Orientation Failures in MLLM-as-a-Judge Under Cultural Ambiguity](https://mlsanity.com/paper/2606.20676/) (2026)
- [CultureVidBench: Benchmarking Cultural Understanding in Text-to-Video Generation](https://mlsanity.com/paper/2608.01942/) (2026)

## Abstract (arXiv)

Cultural understanding in video means more than recognizing what is visible; it requires grasping the symbolic and temporal significance of cultural concepts. We decompose this into three abilities: naming what a concept symbolizes, visually recognizing it on video, and locating its sub-events in time. Existing video-cultural benchmarks tend to test what is seen, collapsing these three abilities into a single score that hides the bottleneck. We introduce the Cultural Moment Benchmark (CMB): 306 expert-curated concepts from seven countries in Southeast Asia across five categories. We evaluate each concept through three stages, one per ability. Given a description, Stage 1 (S1) selects from four candidate concept names, Stage 2 (S2) selects from four candidate video moments, and Stage 3 (S3) predicts the start and end times of the moment in a video. To keep each stage focused on a distinct ability, we use three design choices: semantic-similarity distractors (S1, S2), unlabeled video moments (S2), and free-form localization on a different example video (S3). Across six vision-language models, failure modes vary by ability and modality. i) Even the strongest closed-source models score below 30% when all three stages must be correct; ii) The three abilities do not fully cascade: naming a concept correctly helps half the models recognize it on video, but recognizing it has little effect on locating the sub-event in time; iii) Audio is complementary, redundant, or distracting depending on the concept, more often distracting in non-Latin-script countries; removing both audio and subtitles hurts Games and Music the most. Our 14-rater human study shows that even Expert raters score below chance on concepts from a neighboring country, indicating that CMB requires country-specific cultural knowledge. CMB acts as a diagnostic harness, attributing failures to a specific ability or modality.