# VCA: Video Curious Agent for Long Video Understanding

arXiv: https://arxiv.org/abs/2412.10471 · HTML: https://mlsanity.com/paper/2412.10471/

## Key facts

- Cited by 56 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 66 of its 80 references that are in the map.
- First posted on arXiv 12 Dec 2024.

## 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 66)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [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)
- [Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context](https://mlsanity.com/paper/2403.05530/) (2024)
- [SlowFast Networks for Video Recognition](https://mlsanity.com/paper/1812.03982/) (2018)
- [MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models](https://mlsanity.com/paper/2306.13394/) (2023)
- [Is Space-Time Attention All You Need for Video Understanding?](https://mlsanity.com/paper/2102.05095/) (2021)
- [ViViT: A Video Vision Transformer](https://mlsanity.com/paper/2103.15691/) (2021)
- [Ego4D: Around the World in 3,000 Hours of Egocentric Video](https://mlsanity.com/paper/2110.07058/) (2021)
- [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)
- [VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training](https://mlsanity.com/paper/2203.12602/) (2022)
- [WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents](https://mlsanity.com/paper/2207.01206/) (2022)
- [OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environments](https://mlsanity.com/paper/2404.07972/) (2024)
- [MVBench: A Comprehensive Multi-modal Video Understanding Benchmark](https://mlsanity.com/paper/2311.17005/) (2023)
- [How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites](https://mlsanity.com/paper/2404.16821/) (2024)
- [Chameleon: Mixed-Modal Early-Fusion Foundation Models](https://mlsanity.com/paper/2405.09818/) (2024)
- [Temporal Segment Networks: Towards Good Practices for Deep Action Recognition](https://mlsanity.com/paper/1608.00859/) (2016)
- [VideoLLaMA 2: Advancing Spatial-Temporal Modeling and Audio Understanding in Video-LLMs](https://mlsanity.com/paper/2406.07476/) (2024)
- [CogAgent: A Visual Language Model for GUI Agents](https://mlsanity.com/paper/2312.08914/) (2023)
- [VideoBERT: A Joint Model for Video and Language Representation Learning](https://mlsanity.com/paper/1904.01766/) (2019)
- [EgoSchema: A Diagnostic Benchmark for Very Long-form Video Language Understanding](https://mlsanity.com/paper/2308.09126/) (2023)
- [LongVideoBench: A Benchmark for Long-context Interleaved Video-Language Understanding](https://mlsanity.com/paper/2407.15754/) (2024)
- [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)
- [Rethinking Spatiotemporal Feature Learning: Speed-Accuracy Trade-offs in Video Classification](https://mlsanity.com/paper/1712.04851/) (2017)
- [Long Context Transfer from Language to Vision](https://mlsanity.com/paper/2406.16852/) (2024)

## Built on by (most cited first, 30 of 56)

- [Deep Video Discovery: Agentic Search with Tool Use for Long-form Video Understanding](https://mlsanity.com/paper/2505.18079/) (2025)
- [LVAgent: Long Video Understanding by Multi-Round Dynamical Collaboration of MLLM Agents](https://mlsanity.com/paper/2503.10200/) (2025)
- [ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding](https://mlsanity.com/paper/2506.01300/) (2025)
- [VideoChat-A1: Thinking with Long Videos by Chain-of-Shot Reasoning](https://mlsanity.com/paper/2506.06097/) (2025)
- [ReWatch-R1: Boosting Complex Video Reasoning in Large Vision-Language Models through Agentic Data Synthesis](https://mlsanity.com/paper/2509.23652/) (2025)
- [VideoARM: Agentic Reasoning over Hierarchical Memory for Long-Form Video Understanding](https://mlsanity.com/paper/2512.12360/) (2025)
- [Think3D: Thinking with Space for Spatial Reasoning](https://mlsanity.com/paper/2601.13029/) (2026)
- [LVOmniBench: Pioneering Long Audio-Video Understanding Evaluation for Omnimodal LLMs](https://mlsanity.com/paper/2603.19217/) (2026)
- [Why Reasoning Matters? A Survey of Advancements in Multimodal Reasoning (v1)](https://mlsanity.com/paper/2504.03151/) (2025)
- [MR. Video: "MapReduce" is the Principle for Long Video Understanding](https://mlsanity.com/paper/2504.16082/) (2025)
- [VideoLucy: Deep Memory Backtracking for Long Video Understanding](https://mlsanity.com/paper/2510.12422/) (2025)
- [VideoSeek: Long-Horizon Video Agent with Tool-Guided Seeking](https://mlsanity.com/paper/2603.20185/) (2026)
- [LensWalk: Agentic Video Understanding by Planning How You See in Videos](https://mlsanity.com/paper/2603.24558/) (2026)
- [Active Perception Agent for Omnimodal Audio-Video Understanding](https://mlsanity.com/paper/2512.23646/) (2025)
- [Divide, then Ground: Adapting Frame Selection to Query Types for Long-Form Video Understanding](https://mlsanity.com/paper/2512.04000/) (2025)
- [Tool-Augmented Spatiotemporal Reasoning for Streamlining Video Question Answering Task](https://mlsanity.com/paper/2512.10359/) (2025)
- [VideoPASTA: 7K Preference Pairs That Matter for Video-LLM Alignment](https://mlsanity.com/paper/2504.14096/) (2025)
- [Think, Then Verify: A Hypothesis-Verification Multi-Agent Framework for Long Video Understanding](https://mlsanity.com/paper/2603.04977/) (2026)
- [FrameOracle: Learning What to See and How Much to See in Videos](https://mlsanity.com/paper/2510.03584/) (2025)
- [LongVideo-R1: Smart Navigation for Low-cost Long Video Understanding](https://mlsanity.com/paper/2602.20913/) (2026)
- [AViLA: Asynchronous Vision-Language Agent for Streaming Multimodal Data Interaction](https://mlsanity.com/paper/2506.18472/) (2025)
- [MASR: Self-Reflective Reasoning through Multimodal Hierarchical Attention Focusing for Agent-based Video Understanding](https://mlsanity.com/paper/2504.17213/) (2025)
- [AVA: Towards Agentic Video Analytics with Vision Language Models](https://mlsanity.com/paper/2505.00254/) (2025)
- [Video Understanding: From Geometry and Semantics to Unified Models](https://mlsanity.com/paper/2603.17840/) (2026)
- [Where to Look Matters: On-Policy Self-Distillation for Long-Video Understanding](https://mlsanity.com/paper/2608.25356/) (2026)
- [GCAgent: Long-Video Understanding via Schematic and Narrative Episodic Memory](https://mlsanity.com/paper/2511.12027/) (2025)
- [Video-QTR: Query-Driven Temporal Reasoning Framework for Lightweight Video Understanding](https://mlsanity.com/paper/2512.09354/) (2025)
- [HiMu: Hierarchical Multimodal Frame Selection for Long Video Question Answering](https://mlsanity.com/paper/2603.18558/) (2026)
- [SVAgent: Storyline-Guided Long Video Understanding via Cross-Modal Multi-Agent Collaboration](https://mlsanity.com/paper/2604.05079/) (2026)
- [Native Active Perception as Reasoning for Omni-Modal Understanding](https://mlsanity.com/paper/2606.19341/) (2026)

## Abstract (arXiv)

Long video understanding poses unique challenges due to their temporal complexity and low information density. Recent works address this task by sampling numerous frames or incorporating auxiliary tools using LLMs, both of which result in high computational costs. In this work, we introduce a curiosity-driven video agent with self-exploration capability, dubbed as VCA. Built upon VLMs, VCA autonomously navigates video segments and efficiently builds a comprehensive understanding of complex video sequences. Instead of directly sampling frames, VCA employs a tree-search structure to explore video segments and collect frames. Rather than relying on external feedback or reward, VCA leverages VLM's self-generated intrinsic reward to guide its exploration, enabling it to capture the most crucial information for reasoning. Experimental results on multiple long video benchmarks demonstrate our approach's superior effectiveness and efficiency.