[2604.12890] Towards Long-horizon Agentic Multimodal Search

Computer Science > Computer Vision and Pattern Recognition arXiv:2604.12890 (cs) [Submitted on 14 Apr 2026] Title:Towards Long-horizon Agentic Multimodal Search Authors:Yifan Du, Zikang Liu, Jinbiao Peng, Jie Wu, Junyi Li, Jinyang Li, Wayne Xin Zhao, Ji-Rong Wen View a PDF of the paper titled Towards Long-horizon Agentic Multimodal Search, by Yifan Du and 7 other authors View PDF HTML (experimental) Abstract:Multimodal deep search agents have shown great potential in solving complex tasks by iteratively collecting textual and visual evidence. However, managing the heterogeneous information and high token costs associated with multimodal inputs over long horizons remains a critical challenge, as existing methods often suffer from context explosion or the loss of crucial visual signals. To address this, we propose a novel Long-horizon MultiModal deep search framework, named LMM-Searcher, centered on a file-based visual representation mechanism. By offloading visual assets to an external file system and mapping them to lightweight textual identifiers (UIDs), our approach mitigates context overhead while preserving multimodal information for future access. We equip the agent with a tailored fetch-image tool, enabling a progressive, on-demand visual loading strategy for active perception. Furthermore, we introduce a data synthesis pipeline designed to generate queries requiring complex cross-modal multi-hop reasoning. Using this pipeline, we distill 12K high-quality trajectorie...