# Watching, Reasoning, and Searching: A Video Deep Research Benchmark on Open Web for Agentic Video Reasoning

arXiv: https://arxiv.org/abs/2601.06943 · HTML: https://mlsanity.com/paper/2601.06943/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Placed in Multimodal reasoning, one of 17 directions in LLM reasoning.
- Builds on 22 of its 35 references that are in the map.
- First posted on arXiv 11 Jan 2026.

## Placed in

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

## Builds on (most cited first, 22 of 22)

- [Retrieval-Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2312.10997/) (2023)
- [InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency](https://mlsanity.com/paper/2508.18265/) (2025)
- [Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning](https://mlsanity.com/paper/2503.09516/) (2025)
- [DeepSeek-V3 Technical Report](https://mlsanity.com/paper/2412.19437/) (2024)
- [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)
- [WebThinker: Empowering Large Reasoning Models with Deep Research Capability](https://mlsanity.com/paper/2504.21776/) (2025)
- [DeepResearcher: Scaling Deep Research via Reinforcement Learning in Real-world Environments](https://mlsanity.com/paper/2504.03160/) (2025)
- [WebWalker: Benchmarking LLMs in Web Traversal](https://mlsanity.com/paper/2501.07572/) (2025)
- [ReSearch: Learning to Reason with Search for LLMs via Reinforcement Learning](https://mlsanity.com/paper/2503.19470/) (2025)
- [SimpleTIR: End-to-End Reinforcement Learning for Multi-Turn Tool-Integrated Reasoning](https://mlsanity.com/paper/2509.02479/) (2025)
- [Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems](https://mlsanity.com/paper/2504.01990/) (2025)
- [MMSearch: Benchmarking the Potential of Large Models as Multi-modal Search Engines](https://mlsanity.com/paper/2409.12959/) (2024)
- [Deep Video Discovery: Agentic Search with Tool Use for Long-form Video Understanding](https://mlsanity.com/paper/2505.18079/) (2025)
- [VideoVista: A Versatile Benchmark for Video Understanding and Reasoning](https://mlsanity.com/paper/2406.11303/) (2024)
- [LongVT: Incentivizing "Thinking with Long Videos" via Native Tool Calling](https://mlsanity.com/paper/2511.20785/) (2025)
- [LVAgent: Long Video Understanding by Multi-Round Dynamical Collaboration of MLLM Agents](https://mlsanity.com/paper/2503.10200/) (2025)
- [SVBench: A Benchmark with Temporal Multi-Turn Dialogues for Streaming Video Understanding](https://mlsanity.com/paper/2502.10810/) (2025)
- [VideoWebArena: Evaluating Long Context Multimodal Agents with Video Understanding Web Tasks](https://mlsanity.com/paper/2410.19100/) (2024)
- [Neptune: The Long Orbit to Benchmarking Long Video Understanding](https://mlsanity.com/paper/2412.09582/) (2024)
- [VRBench: A Benchmark for Multi-Step Reasoning in Long Narrative Videos](https://mlsanity.com/paper/2506.10857/) (2025)
- [Video-Browser: Towards Agentic Open-web Video Browsing](https://mlsanity.com/paper/2512.23044/) (2025)

## Built on by (most cited first, 14 of 14)

- [A Survey of Agentic Reasoning for Large Language Models: Towards Recursively Self-Improving and Collective Agents](https://mlsanity.com/paper/2601.12538/) (2026)
- [OmniGAIA: Towards Native Omni-Modal AI Agents](https://mlsanity.com/paper/2602.22897/) (2026)
- [VideoSearcher: Empowering Video Deep Research with Multi-Tool Agentic Reasoning via Reinforcement Learning](https://mlsanity.com/paper/2607.02927/) (2026)
- [Agentic Active Omni-Modal Perception for Multi-Hop Audio-Visual Reasoning](https://mlsanity.com/paper/2605.28192/) (2026)
- [From Where Things Are to What They Are For: Benchmarking Spatial-Functional Intelligence in Multimodal LLMs](https://mlsanity.com/paper/2605.02130/) (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)
- [Structured Evidence Selection for Weakly Supervised Video Anomaly Detection](https://mlsanity.com/paper/2607.10298/) (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)
- [Agentic Video Understanding: A Survey](https://mlsanity.com/paper/2609.31713/) (2026)
- [OneSearch-VL: Unified Multimodal Deep Research Agent for Image and Video](https://mlsanity.com/paper/2610.12419/) (2026)

## Abstract (arXiv)

In real-world video question answering scenarios, videos often provide only localized visual cues, while verifiable answers are distributed across the open web; models therefore need to jointly perform cross-frame clue extraction, iterative retrieval, and multi-hop reasoning-based verification. To bridge this gap, we construct the first video deep research benchmark, VideoDR. VideoDR centers on video-conditioned open-domain video question answering, requiring cross-frame visual anchor extraction, interactive web retrieval, and multi-hop reasoning over joint video-web evidence; through rigorous human annotation and quality control, we obtain high-quality video deep research samples spanning six semantic domains. We evaluate multiple closed-source and open-source multimodal large language models under both the Workflow and Agentic paradigms, and the results show that Agentic is not consistently superior to Workflow: its gains depend on a model's ability to maintain the initial video anchors over long retrieval chains. Further analysis indicates that goal drift and long-horizon consistency are the core bottlenecks. In sum, VideoDR provides a systematic benchmark for studying video agents in open-web settings and reveals the key challenges for next-generation video deep research agents.