# Needle In A Video Haystack: A Scalable Synthetic Evaluator for Video MLLMs

arXiv: https://arxiv.org/abs/2406.09367 · HTML: https://mlsanity.com/paper/2406.09367/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- Placed in Video understanding with MLLMs, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 34 of its 55 references that are in the map.
- First posted on arXiv 13 Jun 2024.

## Placed in

- Multimodal LLMs / vision-language models / [Video understanding with MLLMs](https://mlsanity.com/mllm/video-understanding-with-mllms/)

## Builds on (most cited first, 30 of 34)

- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023)
- [Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context](https://mlsanity.com/paper/2403.05530/) (2024)
- [Sigmoid Loss for Language Image Pre-Training](https://mlsanity.com/paper/2303.15343/) (2023)
- [Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding](https://mlsanity.com/paper/2306.02858/) (2023)
- [Video-MME: The First-Ever Comprehensive Evaluation Benchmark of Multi-modal LLMs in Video Analysis](https://mlsanity.com/paper/2405.21075/) (2024)
- [Video-LLaVA: Learning United Visual Representation by Alignment Before Projection](https://mlsanity.com/paper/2311.10122/) (2023)
- [RULER: What's the Real Context Size of Your Long-Context Language Models?](https://mlsanity.com/paper/2404.06654/) (2024)
- [MVBench: A Comprehensive Multi-modal Video Understanding Benchmark](https://mlsanity.com/paper/2311.17005/) (2023)
- [The "something something" video database for learning and evaluating visual common sense](https://mlsanity.com/paper/1706.04261/) (2017)
- [Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models](https://mlsanity.com/paper/2306.05424/) (2023)
- [VideoChat: Chat-Centric Video Understanding](https://mlsanity.com/paper/2305.06355/) (2023)
- [NExT-QA:Next Phase of Question-Answering to Explaining Temporal Actions](https://mlsanity.com/paper/2105.08276/) (2021)
- [DeepSeek-VL: Towards Real-World Vision-Language Understanding](https://mlsanity.com/paper/2403.05525/) (2024)
- [CogVLM: Visual Expert for Pretrained Language Models](https://mlsanity.com/paper/2311.03079/) (2023)
- [EgoSchema: A Diagnostic Benchmark for Very Long-form Video Language Understanding](https://mlsanity.com/paper/2308.09126/) (2023)
- [ActivityNet-QA: A Dataset for Understanding Complex Web Videos via Question Answering](https://mlsanity.com/paper/1906.02467/) (2019)
- [LLaMA-VID: An Image is Worth 2 Tokens in Large Language Models](https://mlsanity.com/paper/2311.17043/) (2023)
- [MovieChat: From Dense Token to Sparse Memory for Long Video Understanding](https://mlsanity.com/paper/2307.16449/) (2023)
- [TGIF-QA: Toward Spatio-Temporal Reasoning in Visual Question Answering](https://mlsanity.com/paper/1704.04497/) (2017)
- [TempCompass: Do Video LLMs Really Understand Videos?](https://mlsanity.com/paper/2403.00476/) (2024)
- [Perception Test: A Diagnostic Benchmark for Multimodal Video Models](https://mlsanity.com/paper/2305.13786/) (2023)
- [Valley: Video Assistant with Large Language model Enhanced abilitY](https://mlsanity.com/paper/2306.07207/) (2023)
- [Landmark Attention: Random-Access Infinite Context Length for Transformers](https://mlsanity.com/paper/2305.16300/) (2023)
- [LLaVA-Plus: Learning to Use Tools for Creating Multimodal Agents](https://mlsanity.com/paper/2311.05437/) (2023)
- [ST-LLM: Large Language Models Are Effective Temporal Learners](https://mlsanity.com/paper/2404.00308/) (2024)
- [AGQA: A Benchmark for Compositional Spatio-Temporal Reasoning](https://mlsanity.com/paper/2103.16002/) (2021)
- [Video-Bench: A Comprehensive Benchmark and Toolkit for Evaluating Video-based Large Language Models](https://mlsanity.com/paper/2311.16103/) (2023)
- [SEED-Bench-2: Benchmarking Multimodal Large Language Models](https://mlsanity.com/paper/2311.17092/) (2023)
- [ChatBridge: Bridging Modalities with Large Language Model as a Language Catalyst](https://mlsanity.com/paper/2305.16103/) (2023)

## Built on by (most cited first, 30 of 37)

- [Cambrian-S: Towards Spatial Supersensing in Video](https://mlsanity.com/paper/2511.04670/) (2025)
- [T*: Re-thinking Temporal Search for Long-Form Video Understanding](https://mlsanity.com/paper/2504.02259/) (2025)
- [MME-Survey: A Comprehensive Survey on Evaluation of Multimodal LLMs](https://mlsanity.com/paper/2411.15296/) (2024)
- [FrameFusion: Combining Similarity and Importance for Video Token Reduction on Large Vision Language Models](https://mlsanity.com/paper/2501.01986/) (2024)
- [A Survey of Multimodal Retrieval-Augmented Generation](https://mlsanity.com/paper/2504.08748/) (2025)
- [Logic-in-Frames: Dynamic Keyframe Search via Visual Semantic-Logical Verification for Long Video Understanding](https://mlsanity.com/paper/2503.13139/) (2025)
- [Multi-Granular Spatio-Temporal Token Merging for Training-Free Acceleration of Video LLMs](https://mlsanity.com/paper/2507.07990/) (2025)
- [Temporal Chain of Thought: Long-Video Understanding by Thinking in Frames](https://mlsanity.com/paper/2507.02001/) (2025)
- [AV-Reasoner: Improving and Benchmarking Clue-Grounded Audio-Visual Counting for MLLMs](https://mlsanity.com/paper/2506.05328/) (2025)
- [Divide, then Ground: Adapting Frame Selection to Query Types for Long-Form Video Understanding](https://mlsanity.com/paper/2512.04000/) (2025)
- [Scaling the Long Video Understanding of Multimodal Large Language Models via Visual Memory Mechanism](https://mlsanity.com/paper/2603.29252/) (2026)
- [Video-LevelGauge: Investigating Contextual Positional Bias in Large Video Language Models](https://mlsanity.com/paper/2508.19650/) (2025)
- [Video Panels for Long Video Understanding](https://mlsanity.com/paper/2509.23724/) (2025)
- [Movie Facts and Fibs (MF2): A Benchmark for Long Movie Understanding](https://mlsanity.com/paper/2506.06275/) (2025)
- [Video-Zero: Self-Evolution Video Understanding](https://mlsanity.com/paper/2605.14733/) (2026)
- [State-Space Hierarchical Compression with Gated Attention and Learnable Sampling for Hour-Long Video Understanding in Large Multimodal Models](https://mlsanity.com/paper/2506.13564/) (2025)
- [NeMo: Needle in a Montage for Video-Language Understanding](https://mlsanity.com/paper/2509.24563/) (2025)
- [Question-guided Visual Compression with Memory Feedback for Long-Term Video Understanding](https://mlsanity.com/paper/2603.15167/) (2026)
- [Infinite Video Understanding](https://mlsanity.com/paper/2507.09068/) (2025)
- [OmniSparse: Training-Aware Fine-Grained Sparse Attention for Long-Video MLLMs](https://mlsanity.com/paper/2511.12201/) (2025)
- [Reinforce to Learn, Elect to Reason: A Dual Paradigm for Video Reasoning](https://mlsanity.com/paper/2604.04379/) (2026)
- [Which Way Did It Move? Diagnosing and Overcoming Directional Motion Blindness in Video-LLMs](https://mlsanity.com/paper/2605.22823/) (2026)
- [M3Eval: Multi-Modal Memory Evaluation through Cognitively-Grounded Video Tasks](https://mlsanity.com/paper/2606.05008/) (2026)
- [CoRDS: Coreset-based Representative and Diverse Selection for Streaming Video Understanding](https://mlsanity.com/paper/2605.14310/) (2026)
- [Be Indiscrete: The Benefits of Learning Continuous Spine Degeneration Severity Scores](https://mlsanity.com/paper/2607.05090/) (2026)
- [MMLongCite: A Benchmark for Evaluating Fidelity of Long-Context Vision-Language Models](https://mlsanity.com/paper/2510.13276/) (2025)
- [APB-V: Accelerating Long-Video Understanding via Sequence-Parallelism-aware Approximate Attention](https://mlsanity.com/paper/2601.21444/) (2026)
- [InterventionLens: A Multi-Agent Framework for Detecting ASD Intervention Strategies in Parent-Child Shared Reading](https://mlsanity.com/paper/2603.13710/) (2026)
- [AdaSpark: Adaptive Sparsity for Efficient Long-Video Understanding](https://mlsanity.com/paper/2604.08077/) (2026)
- [VGenST-Bench: A Benchmark for Spatio-Temporal Reasoning via Active Video Synthesis](https://mlsanity.com/paper/2605.22570/) (2026)

## Abstract (arXiv)

Video understanding is a crucial next step for multimodal large language models (MLLMs). Various benchmarks are introduced for better evaluating the MLLMs. Nevertheless, current video benchmarks are still inefficient for evaluating video models during iterative development due to the high cost of constructing datasets and the difficulty in isolating specific skills. In this paper, we propose VideoNIAH (Video Needle In A Haystack), a benchmark construction framework through synthetic video generation. VideoNIAH decouples video content from their query-responses by inserting unrelated visual 'needles' into original videos. The framework automates the generation of query-response pairs using predefined rules, minimizing manual labor. The queries focus on specific aspects of video understanding, enabling more skill-specific evaluations. The separation between video content and the queries also allow for increased video variety and evaluations across different lengths. Utilizing VideoNIAH, we compile a video benchmark VNBench, which includes tasks such as retrieval, ordering, and counting to evaluate three key aspects of video understanding: temporal perception, chronological ordering, and spatio-temporal coherence. We conduct a comprehensive evaluation of both proprietary and open-source models, uncovering significant differences in their video understanding capabilities across various tasks. Additionally, we perform an in-depth analysis of the test results and model configurations. Based on these findings, we provide some advice for improving video MLLM training, offering valuable insights to guide future research and model development. The code and data are available at https://github.com/joez17/VideoNIAH.