arXiv AI July 28, 2026 By Jinlong Yang, Wenhao Zhang, Kuanwei Lin, Sijie Cheng

CADER: Confidence-Aware Dynamic Evidence Reasoning for Long-Video Understanding

arXiv:2607. 24582v1 Announce Type: cross Abstract: Long-video understanding increasingly relies on large vision-language models and tool-augmented reasoning, but most systems apply the same inference procedure to every example regardless of difficulty.

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arXiv Computer Vision

Aug 31

Temporal Tree of Thought: Reasoning-Guided Visual Cue Search for Long-Video Understanding

arXiv:2608.27871v1 Announce Type: new Abstract: Long-video understanding remains challenging for Multimodal Large Language Models (MLLMs) due to limited context length. Uniform sampling may miss cruc...

By Ziling Huang, Shin'ichi Satoh

llms agents 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

Jun 2

Towards Sparse Video Understanding and Reasoning

arXiv:2602. 13602v2 Announce Type: replace-cross Abstract: We present \revise (\underline{Re}asoning with \underline{Vi}deo \underline{S}parsity), a multi-round agent for video question answering (VQA).

By Chenwei Xu, Zhen Ye, Shang Wu, Weijian Li, Zihan Wang, Zhuofan Xia, Lie Lu, Pranav Maneriker, Fan Du, Manling Li, Han Liu

llms agents nlp fine-tuning multimodal benchmarks

arXiv Computer Vision

2d ago

Video Evidence Indexing: Learning Where to Look from Video Previews for Token-Budgeted Long-Video Question Answering

arXiv:2610.00757v1 Announce Type: new Abstract: Long-video question answering is limited by the high cost of visual tokens and by the fixed context width of current VLMs. A long-video question may re...

By Haowen Guan, Shengzhi Li, Shichao Pei

nlp efficiency

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

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

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