LongVT: Incentivizing "Thinking with Long Videos" via Native Tool Calling

Zuhao Yang; Sudong Wang; Kaichen Zhang; Keming Wu; Sicong Leng; Yifan Zhang; Bo Li; Chengwei Qin; Shijian Lu; Xingxuan Li; Lidong Bing

arXiv · 2511.20785

LongVT: Incentivizing "Thinking with Long Videos" via Native Tool Calling

Zuhao Yang·Sudong Wang·Kaichen Zhang·Keming Wu·Sicong Leng·Yifan Zhang·Bo Li·Chengwei Qin·Shijian Lu·Xingxuan Li·Lidong Bing

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Abstract

Large multimodal models (LMMs) have shown great potential for video reasoning with textual Chain-of-Thought. However, they remain vulnerable to hallucinations, especially when processing long-form videos where evidence is sparse and temporally dispersed. Inspired by how humans comprehend long videos - by first skimming globally and then examining relevant clips for details - we introduce LongVT, an end-to-end agentic framework that enables "Thinking with Long Videos" via interleaved Multimodal Chain-of-Tool-Thought. Specifically, we exploit LMMs' inherent temporal grounding ability as a native video cropping tool to zoom in on a specific video clip and resample finer-grained video frames. This global-to-local reasoning loop continues until answers are grounded in retrieved visual evidence. Given the scarcity of fine-grained question-answering (QA) data for the long video reasoning task, we curate and will release a data suite named VideoSIAH to facilitate both training and evaluation. Specifically, our training dataset consists of 247.9K samples for tool-integrated cold-start supervised fine-tuning, 1.6K samples for agentic reinforcement learning, and 15.4K samples for agentic reinforcement fine-tuning, respectively. Our evaluation benchmark consists of 1,280 QA pairs that are carefully curated through a semi-automatic data pipeline with human-in-the-loop validation. With a meticulously designed three-stage training strategy and extensive empirical validation, LongVT consistently outperforms existing strong baselines across four challenging long-video understanding and reasoning benchmarks. Our codes, data, and model checkpoints are publicly available at https://github.com/EvolvingLMMs-Lab/LongVT .

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BibTeX RIS

Zuhao Yang, Sudong Wang, Kaichen Zhang, Keming Wu, Sicong Leng, Yifan Zhang, Bo Li, Chengwei Qin, Shijian Lu, Xingxuan Li, Lidong Bing. 2026-05-21. LongVT: Incentivizing "Thinking with Long Videos" via Native Tool Calling. https://arxiv.org/abs/2511.20785

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