arXiv AI September 21, 2026 By Seoyeon An, Hyeonseo Jang, Minsu Kim, Chanho Lee, Younghan Park, Kangwook Lee

AgentVidBench: A Multi-Hop Video Question Answering Benchmark for Evaluating MLLM Agents

AgentVidBench is a new multi‑hop video question‑answering benchmark designed to evaluate spatial, temporal, and causal reasoning in multimodal large language models (MLLMs). Unlike existing tests that focus on simple scene queries or global summaries, AgentVidBench includes step‑by‑step solution traces to assess whether agents gather the necessary evidence to justify their answers. Experiments with 12 MLLMs show limited single‑turn performance, but integrating these models into agentic workflows improves both accuracy and trajectory scores, establishing AgentVidBench as a comprehensive testbed for future research on agentic video understanding.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Computer Vision

Sep 30

A Skill-augmented Agentic Framework and Benchmark for Multi-Video Understanding

arXiv:2603.14733v2 Announce Type: replace Abstract: Multimodal Large Language Models have achieved strong performance in single-video understanding, yet their ability to reason across multiple videos...

By Yue Zhang, Liqiang Jing, Jia Li, Yapeng Tian, Xinya Du, Yunhui Guo, Vibhav Gogate

llms agents computer-vision multimodal benchmarks

arXiv AI

Jul 7

VideoSearcher: Empowering Video Deep Research with Multi-Tool Agentic Reasoning via Reinforcement Learning

arXiv:2607. 02927v1 Announce Type: cross Abstract: Video understanding is moving beyond closed-context perception toward open-world evidence exploration, a paradigm formalized as Video Deep Research (VDR).

By Zhenkun Gao, Yicheng Bao, Jinlong Peng, Xueheng Li, Theo Huang, Bangwei Liu, Kunquan Li, Zhenye Gan, Tao Hu, Chengjun Xie, Mingqian Yang, Xuanhua He, Zhizhong Zhang, Xin Tan, Chengjie Wang, Yuan Xie

llms agents reinforcement-learning multimodal benchmarks

arXiv AI

Sep 15

VideoScout: Learning Agentic Active Exploration with Adaptive Reasoning Pacing for Long Video Understanding

arXiv:2609.15606v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) have achieved remarkable progress on short video understanding yet remain limited on long videos due to the...

By Weixin Xu, Zhenyu Yang, Bing Wang, Shengsheng Qian, Changsheng Xu

llms agents reinforcement-learning fine-tuning multimodal benchmarks safety

arXiv AI

Jun 30

Bridging VideoQA and Video-Guided Agentic Tasks via Generalized Keyframe Extraction

arXiv:2606. 29445v1 Announce Type: cross Abstract: Video understanding is a fundamental capability for multimodal intelligence, and recent Multimodal Large Language Models (MLLMs) have achieved remarkable performance on Video Question Answering (VideoQA) benchmarks.

By Sunqi Fan, Qingle Liu, Runqi Yin, Meng-Hao Guo, Shuojin Yang

llms agents nlp multimodal benchmarks

arXiv Machine Learning

Jul 7

Incentivizing Vision Language Models to Search for Long Video Question Answering

arXiv:2607. 02959v1 Announce Type: cross Abstract: We introduce VSeek, an agentic framework that transforms long-video question answering (LVQA) from a passive, single-pass perception task into a multi-turn retrieval process.

By Harsh Goel, S P Sharan, Sahil Shah, Minkyu Choi, Joungbin An, Kristen Grauman, Sandeep P. Chinchali

llms agents nlp reinforcement-learning benchmarks

arXiv Computer Vision

Oct 22

Agentic Video Understanding: A Survey

The paper surveys emerging video understanding agents that use video as the primary source and actively decide what information to inspect, retain, verify, and act upon. It formalizes an agent loop for video understanding and organizes the literature through a challenge‑to‑design taxonomy linking context bottlenecks, evidence sparsity, temporal causality, and multimodal ambiguity to corresponding design solutions. The survey also reviews state‑space paradigms, learning paradigms, supervision signals, benchmarks, evaluation protocols, and outlines open directions toward agentic‑native temporal modeling and video‑native agents.

By Xinyu Deng, Siwen Luo, Daochang Liu

llms agents efficiency multimodal benchmarks

One email a morning, machine-written

Related stories

A Skill-augmented Agentic Framework and Benchmark for Multi-Video Understanding

VideoSearcher: Empowering Video Deep Research with Multi-Tool Agentic Reasoning via Reinforcement Learning

VideoScout: Learning Agentic Active Exploration with Adaptive Reasoning Pacing for Long Video Understanding

Bridging VideoQA and Video-Guided Agentic Tasks via Generalized Keyframe Extraction

Incentivizing Vision Language Models to Search for Long Video Question Answering

Agentic Video Understanding: A Survey