# MetaSampling: Making Frame Samplers Efficient for Long-Video Question Answering

arXiv: https://arxiv.org/abs/2609.33998 · HTML: https://mlsanity.com/paper/2609.33998/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Video understanding with MLLMs, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 26 of its 30 references that are in the map.
- First posted on arXiv 27 Sep 2026.

## Placed in

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

## Builds on (most cited first, 26 of 26)

- [InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency](https://mlsanity.com/paper/2508.18265/) (2025)
- [Video-MME: The First-Ever Comprehensive Evaluation Benchmark of Multi-modal LLMs in Video Analysis](https://mlsanity.com/paper/2405.21075/) (2024)
- [LongVideoBench: A Benchmark for Long-context Interleaved Video-Language Understanding](https://mlsanity.com/paper/2407.15754/) (2024)
- [VideoLLaMA 3: Frontier Multimodal Foundation Models for Image and Video Understanding](https://mlsanity.com/paper/2501.13106/) (2025)
- [LVBench: An Extreme Long Video Understanding Benchmark](https://mlsanity.com/paper/2406.08035/) (2024)
- [VideoAgent: Long-form Video Understanding with Large Language Model as Agent](https://mlsanity.com/paper/2403.10517/) (2024)
- [VideoTree: Adaptive Tree-based Video Representation for LLM Reasoning on Long Videos](https://mlsanity.com/paper/2405.19209/) (2024)
- [Adaptive Keyframe Sampling for Long Video Understanding](https://mlsanity.com/paper/2502.21271/) (2025)
- [M-LLM Based Video Frame Selection for Efficient Video Understanding](https://mlsanity.com/paper/2502.19680/) (2025)
- [T*: Re-thinking Temporal Search for Long-Form Video Understanding](https://mlsanity.com/paper/2504.02259/) (2025)
- [BOLT: Boost Large Vision-Language Model Without Training for Long-form Video Understanding](https://mlsanity.com/paper/2503.21483/) (2025)
- [Too Many Frames, Not All Useful: Efficient Strategies for Long-Form Video QA](https://mlsanity.com/paper/2406.09396/) (2024)
- [FOCUS: Efficient Keyframe Selection for Long Video Understanding](https://mlsanity.com/paper/2510.27280/) (2025)
- [VideoITG: Multimodal Video Understanding with Instructed Temporal Grounding](https://mlsanity.com/paper/2507.13353/) (2025)
- [MaxInfo: A Training-Free Key-Frame Selection Method Using Maximum Volume for Enhanced Video Understanding](https://mlsanity.com/paper/2502.03183/) (2025)
- [Wavelet-based Frame Selection by Detecting Semantic Boundary for Long Video Understanding](https://mlsanity.com/paper/2603.00512/) (2026)
- [Threading Keyframe with Narratives: MLLMs as Strong Long Video Comprehenders](https://mlsanity.com/paper/2505.24158/) (2025)
- [AdaRD-key: Adaptive Relevance-Diversity Keyframe Sampling for Long-form Video understanding](https://mlsanity.com/paper/2510.02778/) (2025)
- [Event-Anchored Frame Selection for Effective Long-Video Understanding](https://mlsanity.com/paper/2603.00983/) (2026)
- [Towards Fast and Effective Long Video Understanding of Multimodal Large Language Models via Adaptive Quasi-Gaussian Sampling](https://mlsanity.com/paper/2606.24187/) (2026)
- [HiMu: Hierarchical Multimodal Frame Selection for Long Video Question Answering](https://mlsanity.com/paper/2603.18558/) (2026)
- [Query-Conditioned Evidential Keyframe Sampling for MLLM-Based Long-Form Video Understanding](https://mlsanity.com/paper/2604.01002/) (2026)
- [QCA: Query- and Content-Aware Keyframe Selection for Long Video Understanding](https://mlsanity.com/paper/2607.00983/) (2026)
- [ReQuest: Rethinking-based Question-Aware Frame Selection for Long-Form Video QA](https://mlsanity.com/paper/2607.01737/) (2026)
- [LENS: Adaptive Spatio-Temporal Zooming for Keyframe Sampling in Long-Form Videos](https://mlsanity.com/paper/2607.25125/) (2026)
- [Coverage-Driven Adaptive Keyframe Selection for Video Understanding](https://mlsanity.com/paper/2608.00714/) (2026)

## Abstract (arXiv)

Frame selection is an important component of long-video question answering (VQA) with Multimodal Large Language Models (MLLMs). Existing frame-selection methods improve over simple top-k embedding retrieval and uniform sampling, but are typically applied under a fixed global selection budget. We introduce MetaSampling, a training-free, plug-and-play sampling strategy that can be applied on top of existing frame selectors. MetaSampling improves downstream VQA efficiency by dynamically reducing the number of frames passed to the MLLM while preserving, and in some cases improving, answer accuracy. We evaluate MetaSampling across 36 paired frame-selector--MLLM-backbone--VQA-benchmark configurations. MetaSampling reduces the number of selected frames in all 36 configurations and improves accuracy in 25 of them, yielding an average frame reduction of 8.9% while slightly improving accuracy overall.