# Video-Browser: Towards Agentic Open-web Video Browsing

arXiv: https://arxiv.org/abs/2512.23044 · HTML: https://mlsanity.com/paper/2512.23044/

## Key facts

- Cited by 10 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 36 references that are in the map.
- First posted on arXiv 28 Dec 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)

- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities](https://mlsanity.com/paper/2507.06261/) (2025)
- [InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models](https://mlsanity.com/paper/2504.10479/) (2025)
- [WebGPT: Browser-assisted question-answering with human feedback](https://mlsanity.com/paper/2112.09332/) (2021)
- [Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning](https://mlsanity.com/paper/2503.09516/) (2025)
- [GAIA: a benchmark for General AI Assistants](https://mlsanity.com/paper/2311.12983/) (2023)
- [MVBench: A Comprehensive Multi-modal Video Understanding Benchmark](https://mlsanity.com/paper/2311.17005/) (2023)
- [BrowseComp: A Simple Yet Challenging Benchmark for Browsing Agents](https://mlsanity.com/paper/2504.12516/) (2025)
- [Search-o1: Agentic Search-Enhanced Large Reasoning Models](https://mlsanity.com/paper/2501.05366/) (2025)
- [LVBench: An Extreme Long Video Understanding Benchmark](https://mlsanity.com/paper/2406.08035/) (2024)
- [LongVU: Spatiotemporal Adaptive Compression for Long Video-Language Understanding](https://mlsanity.com/paper/2410.17434/) (2024)
- [WebThinker: Empowering Large Reasoning Models with Deep Research Capability](https://mlsanity.com/paper/2504.21776/) (2025)
- [LongVILA: Scaling Long-Context Visual Language Models for Long Videos](https://mlsanity.com/paper/2408.10188/) (2024)
- [MLVU: Benchmarking Multi-task Long Video Understanding](https://mlsanity.com/paper/2406.04264/) (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)
- [VideoChat-Flash: Hierarchical Compression for Long-Context Video Modeling](https://mlsanity.com/paper/2501.00574/) (2024)
- [Video-XL: Extra-Long Vision Language Model for Hour-Scale Video Understanding](https://mlsanity.com/paper/2409.14485/) (2024)
- [NVILA: Efficient Frontier Visual Language Models](https://mlsanity.com/paper/2412.04468/) (2024)
- [BrowseComp-Plus: A More Fair and Transparent Evaluation Benchmark of Deep-Research Agent](https://mlsanity.com/paper/2508.06600/) (2025)
- [WebWatcher: Breaking New Frontier of Vision-Language Deep Research Agent](https://mlsanity.com/paper/2508.05748/) (2025)
- [MMSearch: Benchmarking the Potential of Large Models as Multi-modal Search Engines](https://mlsanity.com/paper/2409.12959/) (2024)
- [VideoRAG: Retrieval-Augmented Generation with Extreme Long-Context Videos](https://mlsanity.com/paper/2502.01549/) (2025)
- [WebWeaver: Structuring Web-Scale Evidence with Dynamic Outlines for Open-Ended Deep Research](https://mlsanity.com/paper/2509.13312/) (2025)
- [VideoWebArena: Evaluating Long Context Multimodal Agents with Video Understanding Web Tasks](https://mlsanity.com/paper/2410.19100/) (2024)
- [Video-XL-2: Towards Very Long-Video Understanding Through Task-Aware KV Sparsification](https://mlsanity.com/paper/2506.19225/) (2025)
- [VideoExplorer: Think With Videos For Agentic Long-Video Understanding](https://mlsanity.com/paper/2506.10821/) (2025)
- [Seeking and Updating with Live Visual Knowledge](https://mlsanity.com/paper/2504.05288/) (2025)
- [MVU-Eval: Towards Multi-Video Understanding Evaluation for Multimodal LLMs](https://mlsanity.com/paper/2511.07250/) (2025)
- [Enhancing Video Large Language Models with Structured Multi-Video Collaborative Reasoning](https://mlsanity.com/paper/2509.13161/) (2025)

## Built on by (most cited first, 10 of 10)

- [A Survey of Agentic Reasoning for Large Language Models: Towards Recursively Self-Improving and Collective Agents](https://mlsanity.com/paper/2601.12538/) (2026)
- [Watching, Reasoning, and Searching: A Video Deep Research Benchmark on Open Web for Agentic Video Reasoning](https://mlsanity.com/paper/2601.06943/) (2026)
- [Sandboxed Coding Agents are Competitive Omni-modal Task Solvers](https://mlsanity.com/paper/2606.00579/) (2026)
- [Omni-DeepSearch: A Benchmark for Audio-Driven Omni-Modal Deep Search](https://mlsanity.com/paper/2605.08762/) (2026)
- [SagaQA: A Multi-hop Reasoning Benchmark for Long-form Narrative Understanding in TV Series](https://mlsanity.com/paper/2606.03301/) (2026)
- [Search-to-World: Evaluation of 3D World Delivery from User Request through Web Search](https://mlsanity.com/paper/2609.07605/) (2026)
- [VideoGAIA: A Benchmark for General AI Assistants on Agentic Video Understanding](https://mlsanity.com/paper/2608.14718/) (2026)
- [Thinking Beyond Videos: Unifying Video Reasoning and Deep Research for Open-World Video Agents](https://mlsanity.com/paper/2608.23329/) (2026)
- [AdaVDR: Adaptive Tool Use and Reflection for Video Deep Research](https://mlsanity.com/paper/2608.25559/) (2026)
- [VideoResearchAgent: Grounded Task Synthesis and Sim-to-Real RL for Open-Web Video Research](https://mlsanity.com/paper/2610.04911/) (2026)

## Abstract (arXiv)

Autonomous agents are shifting information seeking from passive retrieval toward proactive, open-ended research, yet this progress has largely bypassed video, one of the web's richest sources of evidence. We formalize Agentic Video Browsing, in which an agent must query video search engines, decide which returned videos to watch, and answer questions grounded in their visual content. To study this setting reproducibly, we introduce Video-BrowseComp, a benchmark and infrastructure suite with 660 human-annotated questions at three difficulty levels, paired online and offline evaluation tracks, and a fixed video search stack of roughly 12,000 videos spanning over 2,000 hours for evaluation and training-data construction. Our analysis reveals that the main bottleneck lies before video understanding: agents often fail to find the answer-bearing video at all. This bottleneck stems from two coupled challenges. Search engines index textual metadata such as titles and tags, whereas questions often target implicit visual or cross-source evidence absent from these surface forms; and exhaustively inspecting the many returned videos is prohibitively expensive. We propose Video-Browser, an evidence-surfacing agent that improves both sides of search. A Translation Memory helps the Planner phrase queries by analogy to past question-to-query transformations that successfully surfaced their target videos, and a Watcher performs sparse visual screening over returned candidates, retaining only videos that plausibly contain answer-bearing evidence for later dense verification. On the offline track, Video-Browser raises overall accuracy from 11.6% to 27.3% with Qwen3-VL-8B and from 39.8% to 45.1% with Gemini-3.1-Pro over the corresponding ReAct search agents, showing that better query formulation and post-search candidate selection improve end-to-end browsing performance.