arXiv Computer Vision

VideoTIR: Accurate Understanding for Long Videos with Efficient Tool-Integrated Reasoning

VideoTIR introduces a reinforcement‑learning approach to improve long‑video understanding by encouraging multimodal large language models to use comprehensive multi‑level toolkits efficiently. It combines Zero‑RL and SFT cold‑starting strategies to help models retrieve and focus on meaningful video segments, images, and regions, thereby reducing hallucinations. The method includes Toolkit Action Grouped Policy Optimization (TAGPO) to streamline tool‑calling and a sandbox‑based trajectory synthesis framework for high‑quality data, achieving strong results on three long‑video QA benchmarks.

arXiv AI

Aug 28

CounterVid: Counterfactual Video Generation for Mitigating Action and Temporal Hallucinations in Video-Language Models

CounterVid introduces a scalable counterfactual video generation framework that creates videos differing only in actions or temporal structure while keeping scene context intact. The approach uses multimodal LLMs for action proposals and diffusion models for editing, producing a synthetic dataset of ~26k preference pairs for action recognition and sequence ordering. With the MixDPO optimization method, the authors demonstrate significant improvements in action recognition and temporal ordering on Qwen2.5‑VL and InternVL3 backbones, while maintaining overall video understanding.

By Tobia Poppi, Burak Uzkent, Amanmeet Garg, Lucas Porto, Garin Kessler, Yezhou Yang, Marcella Cornia, Lorenzo Baraldi, Rita Cucchiara, Florian Schiffers

llms diffusion multimodal benchmarks safety

arXiv AI

Aug 25

Thinking Beyond Videos: Unifying Video Reasoning and Deep Research for Open-World Video Agents

arXiv:2608.23329v1 Announce Type: cross Abstract: Open-world video understanding often requires a model to locate sparse visual evidence and acquire external knowledge that is absent from the video a...

By Wenqi Liu, Shijie Ma, Yunxiao Wang, Meng Liu, Qile Su, Han Liu, Bohan Hou, Xuanyu Zheng, Changyi Liu, Tianke Zhang, Haonan Fan, Kaiyu Jiang, Yingxin Li, Jiankang Chen, Xu Wang, Bin Wen, Tingting Gao, Han Li, Jianhua Yin, Yinwei Wei, Xuemeng Song

agents reinforcement-learning fine-tuning multimodal benchmarks

arXiv AI

Jul 20

Modularized Dynamic-Granularity Video LLM for Multi-Event Long Video Understanding

arXiv:2607. 15778v1 Announce Type: cross Abstract: Video Large Language Models (Video LLMs) have made significant advancements in various video understanding tasks.

By Wei Feng, Xin Wang, Yu-Wei Zhan, Yuwei Zhou, Wenwu Zhu

llms reinforcement-learning benchmarks

arXiv AI

Jun 30

Tool-Augmented Spatiotemporal Reasoning for Streamlining Video Question Answering Task

arXiv:2512. 10359v1 Announce Type: cross Abstract: Video Question Answering (VideoQA) task serves as a critical playground for evaluating whether foundation models can effectively perceive, understand, and reason about dynamic real-world scenarios.

By Sunqi Fan, Jiashuo Cui, Meng-Hao Guo, Shuojin Yang

llms agents nlp multimodal

arXiv Computer Vision

Aug 21

Video Evidence to Reasoning Efficient Video Understanding via Explicit Evidence Grounding

arXiv:2601. 07761v2 Announce Type: replace Abstract: Large Vision-Language Models (LVLMs) face a fundamental dilemma in video reasoning: they are caught between the prohibitive computational costs of verbose reasoning and the hallucination risks of efficient, ungrounded approaches.

By Yanxiang Huang, Guohua Gao, Zhaoyang Wei

llms reinforcement-learning multimodal benchmarks safety

arXiv Machine Learning

Sep 1

SynMulti: Synthetic-to-Real Learning for Multimodal Video Understanding

arXiv:2604.12335v2 Announce Type: replace-cross Abstract: Training multimodal large language models (MLLMs) for video understanding requires large-scale annotated data spanning diverse tasks such as...

By Tanzila Rahman, Renjie Liao, Leonid Sigal

llms computer-vision nlp fine-tuning multimodal

arXiv Computer Vision

Aug 31

Training-Free Temporal Abstraction for General Video Understanding

The paper introduces STITCH, a training‑free method that partitions videos into semantically meaningful temporal chunks using a frozen video‑text backbone. By detecting changes in the embedding sequence of short video windows, STITCH produces reusable temporal abstractions that can be applied to multiple tasks such as event boundary detection, language‑based moment retrieval, and frame selection for vision‑language models. Experiments show that STITCH performs competitively with specialized methods while requiring no task‑specific training, especially when processing is limited to a few frames or tokens.

By Etienne Casanova, Sevan Brodjian, Pietro Perona

llms rag multimodal

arXiv AI

2d ago

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 12

ReFoCUS: Reinforcement-guided Frame Optimization for Contextual Understanding

arXiv:2506. 01274v2 Announce Type: replace-cross Abstract: Recent progress in Large Multi-modal Models (LMMs) has enabled effective vision-language reasoning, yet the ability to video understanding remains constrained by suboptimal frame selection strategies, albeit with the rapid development of video-specialized LMMs.

By Hosu Lee, Junho Kim, Hyunjun Kim, Yong Man Ro

llms reinforcement-learning multimodal benchmarks

Hugging Face Trending Papers

Aug 24

Thinking Beyond Videos: Unifying Video Reasoning and Deep Research for Open-World Video Agents

Open-world video understanding often requires a model to locate sparse visual evidence and acquire external knowledge that is absent from the video and its parametric memory. While Thinking-with-Video...

agents reinforcement-learning fine-tuning multimodal benchmarks

arXiv Computer Vision

Aug 24

Enhancing Localized Reasoning for Long Video Understanding via Efficient Segment-to-Video Supervision

arXiv:2608.20814v1 Announce Type: new Abstract: Though Multimodal Large Language Models (MLLMs) have shown impressive potential in video understanding, long video understanding (LVU) remains challeng...

By Beibei Zhang, Chao Xu, Jun Lan, Zongyi Li, Lai Wei, Huijia Zhu, Tongwei Ren

llms reinforcement-learning fine-tuning efficiency 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

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