# Agentic Very Long Video Understanding

arXiv: https://arxiv.org/abs/2601.18157 · HTML: https://mlsanity.com/paper/2601.18157/

## Key facts

- Cited by 20 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 10 of its 55 references that are in the map.
- First posted on arXiv 26 Jan 2026.

## Placed in

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

## Builds on (most cited first, 10 of 10)

- [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://mlsanity.com/paper/2501.12948/) (2025)
- [Retrieval-Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2312.10997/) (2023)
- [From Local to Global: A Graph RAG Approach to Query-Focused Summarization](https://mlsanity.com/paper/2404.16130/) (2024)
- [SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features](https://mlsanity.com/paper/2502.14786/) (2025)
- [LightRAG: Simple and Fast Retrieval-Augmented Generation](https://mlsanity.com/paper/2410.05779/) (2024)
- [Project Aria: A New Tool for Egocentric Multi-Modal AI Research](https://mlsanity.com/paper/2308.13561/) (2023)
- [VideoRAG: Retrieval-Augmented Generation with Extreme Long-Context Videos](https://mlsanity.com/paper/2502.01549/) (2025)
- [Ego-R1: Chain-of-Tool-Thought for Ultra-Long Egocentric Video Reasoning](https://mlsanity.com/paper/2506.13654/) (2025)
- [VideoExplorer: Think With Videos For Agentic Long-Video Understanding](https://mlsanity.com/paper/2506.10821/) (2025)
- [CLaMR: Contextualized Late-Interaction for Multimodal Content Retrieval](https://mlsanity.com/paper/2506.06144/) (2025)

## Built on by (most cited first, 20 of 20)

- [Towards Multimodal Lifelong Understanding: A Dataset and Agentic Baseline](https://mlsanity.com/paper/2603.05484/) (2026)
- [Bridging Modalities, Spanning Time: Structured Memory for Ultra-Long Agentic Video Reasoning](https://mlsanity.com/paper/2605.08271/) (2026)
- [Watch, Remember, Reason: Human-View Video Understanding with MLLMs](https://mlsanity.com/paper/2606.07433/) (2026)
- [Personal Visual Context Learning in Large Multimodal Models](https://mlsanity.com/paper/2605.10936/) (2026)
- [StreamArena: Toward Continuous, Interactive, and Long-Horizon Agentic Streaming Video Understanding](https://mlsanity.com/paper/2608.05703/) (2026)
- [Keep It Simple: Multi-Key Episodic Memory Retrieval for Ultra-Long Video Understanding](https://mlsanity.com/paper/2608.07663/) (2026)
- [TraceAV-Bench: Benchmarking Multi-Hop Trajectory Reasoning over Long Audio-Visual Videos](https://mlsanity.com/paper/2605.07593/) (2026)
- [ReTool-Video: Recursive Tool-Using Video Agents with Meta-Augmented Tool Grounding](https://mlsanity.com/paper/2605.13228/) (2026)
- [3rd Place at CVPR 2026 CASTLE Challenge: Agentic Multi-View Long-Context Video Understanding via Hierarchical Knowledge Graph Retrieval](https://mlsanity.com/paper/2606.01933/) (2026)
- [SagaQA: A Multi-hop Reasoning Benchmark for Long-form Narrative Understanding in TV Series](https://mlsanity.com/paper/2606.03301/) (2026)
- [Rethinking RAG in Long Videos: What to Retrieve and How to Use It?](https://mlsanity.com/paper/2606.13141/) (2026)
- [ARGOS: Who, Where, and When in Agentic Multi-Camera Person Search](https://mlsanity.com/paper/2604.12762/) (2026)
- [Semantic and Visual Evidence for Efficient Long-Video Reasoning: A Solution for the HD-EPIC VQA Challenge](https://mlsanity.com/paper/2605.29402/) (2026)
- [ABot-AgentOS: A General Robotic Agent OS with Lifelong Multi-modal Memory](https://mlsanity.com/paper/2607.10350/) (2026)
- [Budget-Constrained Embodied Perception: Four Resource Walls and a Pre-Registered Evaluation of Access-Structured Perception on Open Models at less than 31B](https://mlsanity.com/paper/2608.22975/) (2026)
- [Agentic Video Understanding: A Survey](https://mlsanity.com/paper/2609.31713/) (2026)
- [VideoLoop: Looped Working Memory Against Semantic Thrashing in Long-Form Video Agents](https://mlsanity.com/paper/2609.38119/) (2026)
- [Beyond the Timeline: Augmenting Long-Video Memory with Grounded Entity Biographies](https://mlsanity.com/paper/2609.38155/) (2026)
- [MemLife: Curating and Reasoning over Long-Term Egocentric Video Memories](https://mlsanity.com/paper/2609.40195/) (2026)
- [FlashGaze: Training-Free Multi-Scale Patch Pruning For Efficient Video Understanding](https://mlsanity.com/paper/2610.04225/) (2026)

## Abstract (arXiv)

The advent of always-on personal AI assistants, enabled by all-day wearable devices such as smart glasses, demands a new level of contextual understanding, one that goes beyond short, isolated events to encompass the continuous, longitudinal stream of egocentric video. Achieving this vision requires advances in long-horizon video understanding, where systems must interpret and recall visual and audio information spanning days or even weeks. Existing methods, including large language models and retrieval-augmented generation, are constrained by limited context windows and lack the ability to perform compositional, multi-hop reasoning over very long video streams. In this work, we address these challenges through EGAgent, an enhanced agentic framework centered on entity scene graphs, which represent people, places, objects, and their relationships over time. Our system equips a planning agent with tools for structured search and reasoning over these graphs, as well as hybrid visual and audio search capabilities, enabling detailed, cross-modal, and temporally coherent reasoning. Experiments on the EgoLifeQA and Video-MME (Long) datasets show that our method achieves state-of-the-art performance on EgoLifeQA (57.5%) and competitive performance on Video-MME (Long) (74.1%) for complex longitudinal video understanding tasks. Code is available at https://github.com/facebookresearch/egagent.