# Agentic Keyframe Search for Video Question Answering

arXiv: https://arxiv.org/abs/2503.16032 · HTML: https://mlsanity.com/paper/2503.16032/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Placed in Multimodal and embodied agents, one of 16 directions in LLM agents and tool use.
- Builds on 30 of its 40 references that are in the map.
- First posted on arXiv 20 Mar 2025.

## Placed in

- LLM agents and tool use / [Multimodal and embodied agents](https://mlsanity.com/agents/multimodal-and-embodied-agents/)

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

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://mlsanity.com/paper/2201.12086/) (2022)
- [Reflexion: Language Agents with Verbal Reinforcement Learning](https://mlsanity.com/paper/2303.11366/) (2023)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [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)
- [NExT-QA:Next Phase of Question-Answering to Explaining Temporal Actions](https://mlsanity.com/paper/2105.08276/) (2021)
- [EgoSchema: A Diagnostic Benchmark for Very Long-form Video Language Understanding](https://mlsanity.com/paper/2308.09126/) (2023)
- [Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?](https://mlsanity.com/paper/1711.09577/) (2017)
- [MovieChat: From Dense Token to Sparse Memory for Long Video Understanding](https://mlsanity.com/paper/2307.16449/) (2023)
- [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)
- [VideoAgent: A Memory-augmented Multimodal Agent for Video Understanding](https://mlsanity.com/paper/2403.11481/) (2024)
- [A Simple LLM Framework for Long-Range Video Question-Answering](https://mlsanity.com/paper/2312.17235/) (2023)
- [LongVLM: Efficient Long Video Understanding via Large Language Models](https://mlsanity.com/paper/2404.03384/) (2024)
- [Towards Long-Form Video Understanding](https://mlsanity.com/paper/2106.11310/) (2021)
- [An Image Grid Can Be Worth a Video: Zero-shot Video Question Answering Using a VLM](https://mlsanity.com/paper/2403.18406/) (2024)
- [Self-Evaluation Improves Selective Generation in Large Language Models](https://mlsanity.com/paper/2312.09300/) (2023)
- [Too Many Frames, Not All Useful: Efficient Strategies for Long-Form Video QA](https://mlsanity.com/paper/2406.09396/) (2024)
- [DoraemonGPT: Toward Understanding Dynamic Scenes with Large Language Models (Exemplified as A Video Agent)](https://mlsanity.com/paper/2401.08392/) (2024)
- [LifelongMemory: Leveraging LLMs for Answering Queries in Long-form Egocentric Videos](https://mlsanity.com/paper/2312.05269/) (2023)
- [Zero-Shot Video Question Answering with Procedural Programs](https://mlsanity.com/paper/2312.00937/) (2023)
- [Video-Language Understanding: A Survey from Model Architecture, Model Training, and Data Perspectives](https://mlsanity.com/paper/2406.05615/) (2024)
- [VideoQA in the Era of LLMs: An Empirical Study](https://mlsanity.com/paper/2408.04223/) (2024)
- [Temporal Preference Optimization for Long-Form Video Understanding](https://mlsanity.com/paper/2501.13919/) (2025)
- [Understanding Long Videos with Multimodal Language Models](https://mlsanity.com/paper/2403.16998/) (2024)
- [ViLA: Efficient Video-Language Alignment for Video Question Answering](https://mlsanity.com/paper/2312.08367/) (2023)
- [LinVT: Empower Your Image-level Large Language Model to Understand Videos](https://mlsanity.com/paper/2412.05185/) (2024)
- [Interpolating Video-LLMs: Toward Longer-sequence LMMs in a Training-free Manner](https://mlsanity.com/paper/2409.12963/) (2024)

## Built on by (most cited first, 13 of 13)

- [See&Trek: Training-Free Spatial Prompting for Multimodal Large Language Model](https://mlsanity.com/paper/2509.16087/) (2025)
- [Tool-Augmented Spatiotemporal Reasoning for Streamlining Video Question Answering Task](https://mlsanity.com/paper/2512.10359/) (2025)
- [Video-LMM Post-Training: A Deep Dive into Video Reasoning with Large Multimodal Models](https://mlsanity.com/paper/2510.05034/) (2025)
- [FrameOracle: Learning What to See and How Much to See in Videos](https://mlsanity.com/paper/2510.03584/) (2025)
- [A.I.R.: Enabling Adaptive, Iterative, and Reasoning-based Frame Selection For Video Question Answering](https://mlsanity.com/paper/2510.04428/) (2025)
- [FocusGraph: Graph-Structured Frame Selection for Embodied Long Video Question Answering](https://mlsanity.com/paper/2603.04349/) (2026)
- [Agentic Active Omni-Modal Perception for Multi-Hop Audio-Visual Reasoning](https://mlsanity.com/paper/2605.28192/) (2026)
- [HiCrew: Hierarchical Reasoning for Long-Form Video Understanding via Question-Aware Multi-Agent Collaboration](https://mlsanity.com/paper/2604.21444/) (2026)
- [One Ranking, Any Budget: Matryoshka Evidence-to-Context Frame Selection for Long-Video Understanding](https://mlsanity.com/paper/2608.05707/) (2026)
- [Test-Time Scaling in Multimodal Foundation Models: A Comprehensive Survey of Generation and Reasoning](https://mlsanity.com/paper/2606.08231/) (2026)
- [Bridging VideoQA and Video-Guided Agentic Tasks via Generalized Keyframe Extraction](https://mlsanity.com/paper/2606.29445/) (2026)
- [HAS: Highlight-guided Attention Steering for Multimodal LLM Video Summarization](https://mlsanity.com/paper/2607.17994/) (2026)
- [AgentVidBench: A Multi-Hop Video Question Answering Benchmark for Evaluating MLLM Agents](https://mlsanity.com/paper/2609.21386/) (2026)

## Abstract (arXiv)

Video question answering (VideoQA) enables machines to extract and comprehend key information from videos through natural language interaction, which is a critical step towards achieving intelligence. However, the demand for a thorough understanding of videos and high computational costs still limit the widespread applications of VideoQA. To address it, we propose Agentic Keyframe Search (AKeyS), a simple yet powerful algorithm for identifying keyframes in the VideoQA task. It can effectively distinguish key information from redundant, irrelevant content by leveraging modern language agents to direct classical search algorithms. Specifically, we first segment the video and organize it as a tree structure. Then, AKeyS uses a language agent to estimate heuristics and movement costs while dynamically expanding nodes. Finally, the agent determines if sufficient keyframes have been collected based on termination conditions and provides answers. Extensive experiments on the EgoSchema and NExT-QA datasets show that AKeyS outperforms all previous methods with the highest keyframe searching efficiency, which means it can accurately identify key information and conduct effective visual reasoning with minimal computational overhead. For example, on the EgoSchema subset, it achieves 1.8% higher accuracy while processing only 43.5% of the frames compared to VideoTree. We believe that AKeyS represents a significant step towards building intelligent agents for video understanding. The code is publicly available at https://github.com/fansunqi/AKeyS.