[2601.22060] Vision-DeepResearch: Incentivizing DeepResearch Capability in Multimodal Large Language Models

Computer Science > Computer Vision and Pattern Recognition arXiv:2601.22060 (cs) [Submitted on 29 Jan 2026 (v1), last revised 28 Feb 2026 (this version, v2)] Title:Vision-DeepResearch: Incentivizing DeepResearch Capability in Multimodal Large Language Models Authors:Wenxuan Huang, Yu Zeng, Qiuchen Wang, Zhen Fang, Shaosheng Cao, Zheng Chu, Qingyu Yin, Shuang Chen, Zhenfei Yin, Lin Chen, Zehui Chen, Xu Tang, Yao Hu, Philip Torr, Feng Zhao, Wanli Ouyang View a PDF of the paper titled Vision-DeepResearch: Incentivizing DeepResearch Capability in Multimodal Large Language Models, by Wenxuan Huang and 15 other authors View PDF HTML (experimental) Abstract:Multimodal large language models (MLLMs) have achieved remarkable success across a broad range of vision tasks. However, constrained by the capacity of their internal world knowledge, prior work has proposed augmenting MLLMs by ``reasoning-then-tool-call'' for visual and textual search engines to obtain substantial gains on tasks requiring extensive factual information. However, these approaches typically define multimodal search in a naive setting, assuming that a single full-level or entity-level image query and few text query suffices to retrieve the key evidence needed to answer the question, which is unrealistic in real-world scenarios with substantial visual noise. Moreover, they are often limited in the reasoning depth and search breadth, making it difficult to solve complex questions that require aggregating evidence f...