DeepVoyager-VL: Incentivizing Vision-in-the-Loop Search for Long-Horizon Multimodal Agents

DeepVoyager-VL presents a long-horizon multimodal deep-search framework for open-world problems. It places visual evidence inside the search loop, allowing intermediate images to guide continued retrieval and reasoning rather than restricting vision to input or answer stages. The framework uses a multimodal event graph to synthesize tasks with visual dependencies and long reasoning chains, together with active visual acquisition and on-demand image loading. The authors fine-tune models on the synthesized data without reinforcement learning and report effectiveness across ten multimodal search benchmarks, although the supplied abstract does not provide numerical results, baselines, or model details.