# Think-Clip-Sample: Slow-Fast Frame Selection for Video Understanding

arXiv: https://arxiv.org/abs/2601.11359 · HTML: https://mlsanity.com/paper/2601.11359/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in Multimodal reasoning, one of 17 directions in LLM reasoning.
- Builds on 8 of its 25 references that are in the map.
- First posted on arXiv 16 Jan 2026.

## Placed in

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

## Builds on (most cited first, 8 of 8)

- [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://mlsanity.com/paper/2501.12948/) (2025)
- [Qwen2.5-VL Technical Report](https://mlsanity.com/paper/2502.13923/) (2025)
- [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024)
- [Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution](https://mlsanity.com/paper/2409.12191/) (2024)
- [OpenAI o1 System Card](https://mlsanity.com/paper/2412.16720/) (2024)
- [Seed1.5-VL Technical Report](https://mlsanity.com/paper/2505.07062/) (2025)
- [Think Silently, Think Fast: Dynamic Latent Compression of LLM Reasoning Chains](https://mlsanity.com/paper/2505.16552/) (2025)
- [iMOVE: Instance-Motion-Aware Video Understanding](https://mlsanity.com/paper/2502.11594/) (2025)

## Built on by (most cited first, 4 of 4)

- [PEEK: Picking Essential frames via Efficient Knowledge distillation](https://mlsanity.com/paper/2605.31029/) (2026)
- [Video2Code: Generating Interactive Webpages from UI Videos via Action-Aware Revisit](https://mlsanity.com/paper/2606.20711/) (2026)
- [FORGE: Frame Orthogonality in Relevance Geometry for Long-Form Video Understanding](https://mlsanity.com/paper/2607.25266/) (2026)
- [FORTE: Adaptive Scoring and Exact Keyframe Selection for Long-Video Question Answering](https://mlsanity.com/paper/2610.00573/) (2026)

## Abstract (arXiv)

Recent progress in multi-modal large language models (MLLMs) has significantly advanced video understanding. However, their performance on long-form videos remains limited by computational constraints and suboptimal frame selection. We present Think-Clip-Sample (TCS), a training-free framework that enhances long video understanding through two key components: (i) Multi-Query Reasoning, which generates multiple queries to capture complementary aspects of the question and video; and (ii) Clip-level Slow-Fast Sampling, which adaptively balances dense local details and sparse global context. Extensive experiments on MLVU, LongVideoBench, and VideoMME demonstrate that TCS consistently improves performance across different MLLMs, boosting up to 6.9% accuracy, and is capable of achieving comparable accuracy with 50% fewer inference time cost, highlighting both efficiency and efficacy of TCS on long video understanding.