Towards Long-horizon Agentic Multimodal Search
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 trajectories to fine-tune Qwen3-VL-Thinking-30A3B into a specialized multimodal deep search agent. Extensive experiments across four benchmarks demonstrate that our method successfully scales to 100-turn search horizons, achieving state-of-the-art performance among open-source models on challenging long-horizon benchmarks like MM-BrowseComp and MMSearch-Plus, while also exhibiting strong generalizability across different base models. Our code will be released in https://github.com/RUCAIBox/LMM-Searcher.
69df022dd3c84fcbf2921843
Yifan Du
69df022dd3c84fcbf2921844
Zikang Liu
69df022dd3c84fcbf2921845
Jinbiao Peng
69df022dd3c84fcbf2921846
Jie Wu
claimed_verified
2026-04-15T11:50:43.162Z
696c829c3adc65f502527e37
/avatars/d4656368e2b549e21d1f88cf4700454f.svg
jie wu
jie132
user
jie132
69df022dd3c84fcbf2921847
Junyi Li
69df022dd3c84fcbf2921848
Jinyang Li
69df022dd3c84fcbf2921849
Wayne Xin Zhao
69df022dd3c84fcbf292184a
Ji-Rong Wen
Towards Long-horizon Agentic Multimodal Search
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 trajectories to fine-tune Qwen3-VL-Thinking-30A3B into a specialized multimodal deep search agent. Extensive experiments across four benchmarks demonstrate that our method successfully scales to 100-turn search horizons, achieving state-of-the-art performance among open-source models on challenging long-horizon benchmarks like MM-BrowseComp and MMSearch-Plus, while also exhibiting strong generalizability across different base models. Our code will be released in https://github.com/RUCAIBox/LMM-Searcher.
A novel long-horizon multimodal deep search framework called LMM-Searcher is introduced, featuring a file-based visual representation mechanism and progressive visual loading to handle heterogeneous information and high token costs effectively.
multimodal deep search agents visual representation mechanism file-based visual representation lightweight textual identifiers UID fetch-image tool active perception data synthesis pipeline cross-modal multi-hop reasoning Qwen3-VL-Thinking-30A3B MM-BrowseComp MMSearch-Plus