DeepVoyager-VL: Building Long-Horizon Multimodal Agents Without RL
Huanyao Zhang · hf · 2026-08-04
While Multimodal LLMs excel at visual understanding, their static parametric knowledge limits them in dynamic open-world problems. Current multimodal deep search methods typically confine vision to the input or answer stages, ignoring its role in intermediate reasoning and limiting interaction depth.
To address this, researchers propose DeepVoyager-VL, a long-horizon multimodal deep-search framework featuring vision-in-the-loop search. Key mechanisms include:
- Data Synthesis: Constructs a multimodal event graph to generate training data with intermediate visual dependencies and long reasoning chains.
- Agent Framework: Designs an interaction mechanism for active visual acquisition and on-demand image loading.
- Model Fine-tuning: Fine-tunes models directly on synthesized data without requiring reinforcement learning.
Extensive experiments across ten multimodal search benchmarks validate the effectiveness of this approach.
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